Crystal is a new email technology based on empathy. Through the use of public information, the program aims to help users communicate more effectively, and efficiently, by providing email, speaking and selling tips tailored to recipients preferred communication styles and personalities.
After examining the user onboarding process that Crystal is currently using to convert interested users into full time paying customers, the apprenticeship team at Fresh Tilled Soil was tasked with creating a visual design to move the product to the next level.
With the technology being a priority from the start, the visual design was a secondary focus when creating the product.
I created the design system using Photoshop and presented it to the finder for review. I also continued to be the client's main point of contact as I, and other team members, navigated the project's deadlines.
In order to create a visual asthetic that can be manipulated as the site, and product grows, I created a visual system using modular content elements that can be used for multiple types of content and moved/removed as needed.
Colors were pulled from the logo, and supplemented with complementary blues and a tertiary orangy red for emphasis (alerts and completed actions). Different shades and stroke widths are used to signal button and input states.
In order to create a feeling of openness and trustwortyness, Lato (a Google Webfont) was chosen. Its various weights provide options for emphasis, headlings, body text, etc.
A twelve column grid grounds the design and allows for easy scalability to medium and small screens, or application design if that avenue is pursued in the future.


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Life isn't easy for families in water sparse areas...
Many 3rd World Countries lack acessible clean
water and the only sources are wells that are
that are many miles away!
People are forced to go to great lengths for H2O,
and we'll take you on this journey
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Border Closure: Cleric Urges FG To Consider The Sufferings Of Nigerians
by Enioluwa AdeniyiThe Federal Government of Nigeria has been advised by Prophetess Oluwayemisi Sunday-Ajiye, the founder of God Miracle Success Ministry World Wide, to consider the suffering of Nigerians following the closure of land borders in the country.
She stated this while speaking during the 2019 annual anniversary and Thanksgiving ceremony of the ministry in Ado-Ekiti, the Ekiti state capital.
Dignities present during the ceremony include Engr Olajide Borode, Elder Akinlolu Debo, Dr Fasoranti Ademola, Pastor Adedayo Folorunso, Pastor Adeyeye Sola and Engr Olajide Borode.
The cleric noted the decision of the government to close the border has been affecting businesses of Nigerians since August.
She further stated that the government must provide an enabling environment for home-grown rice to be patronised.
“Government should always consider the plights of the downtrodden in the society when taken sensitive decisions. Our leaders should move closer to God for Him to direct them in governance.”
“No doubt, the border closure has affected some persons in their businesses and I want to say again for the government to consider the people. Also, we need more industries so as to help more people to be engaged.”
She charged spiritual heads in the country to be more prayerful and work together in unity in the overall interest of the country, she noted that many Nigerians are looking up to the spiritual leaders for a change in the country.


8 May 2009 - As of 06:00 GMT, 8 May 2009, 24 countries have officially reported 2384 cases of influenza A (H1N1) infection.
Mexico has reported 1112 laboratory confirmed human cases of infection, including 42 deaths. The United States has reported 896 laboratory confirmed human cases, including two deaths.
The following countries have reported laboratory confirmed cases with no deaths - Austria (1), Canada (214), China, Hong Kong Special Administrative Region (1), Colombia (1), Costa Rica (1), Denmark (1), El Salvador (2), France (5), Germany (10), Guatemala (1), Ireland (1), Israel (6), Italy (5), Netherlands (2), New Zealand (5), Poland (1), Portugal (1), Republic of Korea (3), Spain (81), Sweden (1), Switzerland (1) and the United Kingdom (32).
WHO is not recommending travel restrictions related to the outbreak of the influenza A(H1N1) virus.
Individuals who are ill should delay travel plans and returning travelers who fall ill should seek appropriate medical care. These recommendations are prudent measures which can limit the spread of many communicable diseases, including influenza.
Further information on the situation will be available on the WHO website on a regular basis.


Who is conducting this study?
The Surfrider Foundation is conducting the study in partnership with Ecotrust, Monmouth University’s Urban Coast Institute, and The Nature Conservancy.
Who should fill out the survey?
Beach goers, swimmers, beach wildlife viewers, surfers, kayakers, windsurfers, divers, and other “non-consumptive” recreational users should complete the survey. Separate studies are focusing on uses like fishing and boating.
How do I participate?
Participation is simple: go to http://www.surfrider.org/mid-atlantic-recreation and enter your email address. A unique survey link will then be sent to you via email. You can then fill out the survey at your own pace, saving as you go.
Why should I participate?
Your help is needed to ensure that your favorite recreation areas are considered in coastal and ocean planning. Recreational uses are poorly mapped and data is limited. To minimize conflict and maximize efficiency between different coastal and ocean uses, MARCO is developing an online mapping tool to support ocean planning in the Mid-Atlantic region. To ensure that the interests and safety of recreational users are considered in management plans, better information is needed.
What is ocean planning?
As new coastal and ocean uses develop, it is becoming clear to resource managers that an over-arching organizational plan is needed. Currently areas are managed by a patchwork of state and federal agencies. Ocean planning will allow different uses like fishing, diving, and shipping to coexist by designating specific areas for them, not unlike land-based zoning.
Will my favorite coastal or ocean recreation spot be revealed to everyone else?
No, the data used to produce spatial maps will be aggregated so as to not reveal individual locations. The data will not be posted on recreational user websites or shared with travel guides, etc.
What is MARCO and how does it relate to this study?
The Mid-Atlantic Regional Council on the Ocean, or MARCO, is an arrangement by the Governors of New York, New Jersey, Delaware, Maryland, and Virginia to collaborate in advancing goals to protect our ocean and coast. To further these goals, MARCO created the Ocean Data Portal, an online tool to organize available data from the region. The data from this study will be placed on the Ocean Data Portal. MARCO is also organizing a similar study to this one, focused solely on recreational boating.


Affiliate Marketing Template
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This is a preview build of Jetroid and may contain bugs, be completely unbalanced, or not correctly save your progress. Play at your own risk.
Left/Up/Right or A/W/D keys move. Esc pauses the game and Q quits while a game is paused. Try to collect as much as you can find before your air runs. Fly into the exit at any time to end the game.


[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]
Re: java.rmi.NotBoundException (Ganymede)
|
> From: "Tippe, Darrell"<EMAIL_ADDRESS>> To<EMAIL_ADDRESS><EMAIL_ADDRESS>> Subject: Ganymede
> Date: Tue, 13 Jul 1999 11:47:04 -0600
>
> Hi Jon, I am consultant for ATCO I-tek formally know as Canadian Utilities
> (CU). We have Ganymede running on our infrastructure here and was setup by
> a person of the name Curtis King. I am getting a error message when I try
> and run the Ganymede GUI. Keep in mind that the GUI has been working well
> and just stopped on Friday July 13th.
Friday, July 9th?
> The error message is as follows:
> RMI: Couldn't bind to server object java.rmi.NotBoundException:
> ganymede.server
What this indicates is that the client was able to talk to the RMI
registry on the machine running the Ganymede server, but that the
RMI registry doesn't know about any 'ganymede.server' object, which
is the name that the Ganymede server uses to register itself with
RMI.
I would expect to see this if the Ganymede server was killed, or if the RMI
registry process was killed and restarted.
You can also see this if you are running the RMI registry for Ganymede on a
non-standard port and the client hasn't been configured to look on the actual
port used for RMI, and winds up talking to an RMI registry process installed
to service a different RMI server (Sun's SIMS mail server uses its own
RMI registry process, which won't run properly on a system with Ganymede
unless Ganymede is configured to use an alternate port for the RMI
registry)..
This sort of exception might also be thrown if the RMI registry was aware of a
bound ganymede.server object, but for some reason the RMI registry and/or
client believes that the class files for the server are not compatible with
those included in the client's ganymede.jar file.
> I looked through you discussion list for similar error messages or bug
> reports found some rmi stuff but I do not think it is the same as what I
> have happening here.
>
> Things tried thus far
> Server: Restarted Ganymede server: NIS; inetd; named; anything that might
> be related to this error message.
> We ended up doing a reboot of this server and still did not work.
That's very strange, then... rebooting the system and restarting the Ganymede
server with runServer should have restarted both the RMI registry and the
Ganymede server. I would have to lean on having incompatible versions
of the client and server then..
I did release Ganymede 0.99.4 on Friday.. perhaps someone updated the server
or client without updating both? Note that the installClient process for
0.99.4 no longer installs the web-access files as part of the client's
directory structure, so if you installed 0.99.4 on top of an earlier client
install directory, the html directory would not have been updated, and would
still have the old version of the client jar files in place.
> Client trying to run the GUI on:
> Restarted the same process as above. The only difference is that we have
> yet to reboot the server. Chances are that after the reboot everything will
> go back in order and work again. But I would like to understand why this
> happened.
>
> One thing I read about is that the rmiregistry and the ganymede server are
> not in sync, but when I restart the ganymede server they should sync up
> correctly.
>
> Could you point me in the right direction and shed some insight to this
> problem.
>
> Thank you for your time on this matter.
>
> Take care
> Darrell
-------------------------------------------------------------------------------
Jonathan Abbey<EMAIL_ADDRESS>Applied Research Laboratories The University of Texas at Austin
Ganymede, a free NIS/DNS management system http://www.arlut.utexas.edu/gash2
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{% extends 'base.html' %} {% block content %}
List of measurements campaigns.
| Picture | Campaign | Location | File |
|---|---|---|---|
|
{{ campaign.name }} {{ campaign.description }} |
{{ campaign.latitude }} {{ campaign.longitude }} |
Download |


Package org.drip.spaces.cover
Class CarlStephaniProductBounds
- java.lang.Object
-
- org.drip.spaces.cover.CarlStephaniProductBounds
-
public class CarlStephaniProductBounds extends java.lang.Object
CarlStephaniProductBounds implements the Bounds that result from the Convolution Product Product of 2 Normed Rx To Normed Rx Function Spaces. The References are:
- Carl, B. (1985): Inequalities of the Bernstein-Jackson type and the Degree of Compactness of Operators in Banach Spaces Annals of the Fourier Institute 35 (3) 79-118
- Carl, B., and I. Stephani (1990): Entropy, Compactness, and the Approximation of Operators Cambridge University Press Cambridge UK
- Williamson, R. C., A. J. Smola, and B. Scholkopf (2000): Entropy Numbers of Linear Function Classes, in: Proceedings of the 13th Annual Conference on Computational Learning Theory ACM New York
- Module = Computational Core Module
- Library = Statistical Learning Library
- Project = R1 and Rd Vector/Tensor Spaces (Validated and/or Normed), and Function Classes
- Package = Vector Spaces Covering Number Estimator
- Author:
- Lakshmi Krishnamurthy
-
-
Constructor Summary
Constructors Constructor Description CarlStephaniProductBounds(NormedRxToNormedRxFinite funcClassRxRxA, NormedRxToNormedRxFinite funcClassRxRxB)
CarlStephaniProductBounds Constructor
-
Method Summary
Modifier and Type Method Description NormedRxToNormedRxFinite
funcClassA()
Retrieve the Function Class ANormedRxToNormedRxFinite
funcClassB()
Retrieve the Function Class BCarlStephaniNormedBounds
normedEntropyUpperBound(MaureyOperatorCoveringBounds mocbA, MaureyOperatorCoveringBounds mocbB, int iEntropyNumberIndex, boolean bUseSupremumNorm)
Compute the Normed Upper Entropy Convolution Product Bound across the Function ClassesCarlStephaniNormedBounds
populationMetricEntropyNorm(int iEntropyNumberIndex, boolean bUseSupremumNorm)
Compute the Population Metric Carl-Stephani Entropy Number Upper Bound using either the Metric/Supremum Population Normdouble
populationMetricEntropyNumber(int iEntropyNumberIndexA, int iEntropyNumberIndexB)
Compute the Upper Bound for the Entropy Number of the Operator Population Metric Covering Number Convolution Product Product across both the Function ClassesCarlStephaniNormedBounds
populationSupremumEntropyNorm(int iEntropyNumberIndex, boolean bUseSupremumNorm)
Compute the Population Supremum Carl-Stephani Entropy Number Upper Bound using either the Metric/Supremum Population Normdouble
populationSupremumEntropyNumber(int iEntropyNumberIndexA, int iEntropyNumberIndexB)
Compute the Upper Bound for the Entropy Number of the Operator Population Supremum Covering Number Convolution Product across both the Function ClassesCarlStephaniNormedBounds
sampleMetricEntropyNorm(GeneralizedValidatedVector gvviA, GeneralizedValidatedVector gvviB, int iEntropyNumberIndex, boolean bUseSupremumNorm)
Compute the Sample Metric Carl-Stephani Entropy Number Upper Bound using either the Metric/Supremum Population Normdouble
sampleMetricEntropyNumber(GeneralizedValidatedVector gvviA, GeneralizedValidatedVector gvviB, int iEntropyNumberIndexA, int iEntropyNumberIndexB)
Compute the Upper Bound for the Entropy Number of the Operator Sample Metric Covering Number Convolution Product across both the Function ClassesCarlStephaniNormedBounds
sampleSupremumEntropyNorm(GeneralizedValidatedVector gvviA, GeneralizedValidatedVector gvviB, int iEntropyNumberIndex, boolean bUseSupremumNorm)
Compute the Sample Supremum Carl-Stephani Entropy Number Upper Bound using either the Metric/Supremum Population Normdouble
sampleSupremumEntropyNumber(GeneralizedValidatedVector gvviA, GeneralizedValidatedVector gvviB, int iEntropyNumberIndexA, int iEntropyNumberIndexB)
Compute the Upper Bound for the Entropy Number of the Operator Sample Supremum Covering Number Convolution Product across both the Function Classes
-
-
-
Constructor Detail
-
CarlStephaniProductBounds
public CarlStephaniProductBounds(NormedRxToNormedRxFinite funcClassRxRxA, NormedRxToNormedRxFinite funcClassRxRxB) throws java.lang.Exception
CarlStephaniProductBounds Constructor- Parameters:
funcClassRxRxA
- Function Class AfuncClassRxRxB
- Function Class B- Throws:
java.lang.Exception
- Thrown if the Inputs are Invalid
-
-
Method Detail
-
funcClassA
public NormedRxToNormedRxFinite funcClassA()
Retrieve the Function Class A- Returns:
- The Function Class A
-
funcClassB
public NormedRxToNormedRxFinite funcClassB()
Retrieve the Function Class B- Returns:
- The Function Class B
-
populationMetricEntropyNumber
public double populationMetricEntropyNumber(int iEntropyNumberIndexA, int iEntropyNumberIndexB) throws java.lang.Exception
Compute the Upper Bound for the Entropy Number of the Operator Population Metric Covering Number Convolution Product Product across both the Function Classes- Parameters:
iEntropyNumberIndexA
- Entropy Number Index for Class AiEntropyNumberIndexB
- Entropy Number Index for Class B- Returns:
- The Upper Bound for the Entropy Number of the Operator Population Metric Covering Number Convolution Product Product across both the Function Classes
- Throws:
java.lang.Exception
- Thrown if the Convolution Product Product Population Metric Entropy Number cannot be Computed
-
populationSupremumEntropyNumber
public double populationSupremumEntropyNumber(int iEntropyNumberIndexA, int iEntropyNumberIndexB) throws java.lang.Exception
Compute the Upper Bound for the Entropy Number of the Operator Population Supremum Covering Number Convolution Product across both the Function Classes- Parameters:
iEntropyNumberIndexA
- Entropy Number Index for Class AiEntropyNumberIndexB
- Entropy Number Index for Class B- Returns:
- The Upper Bound for the Entropy Number of the Operator Population Supremum Covering Number Convolution Product across both the Function Classes
- Throws:
java.lang.Exception
- Thrown if the Convolution Product Population Supremum Dyadic Entropy cannot be Computed
-
sampleMetricEntropyNumber
public double sampleMetricEntropyNumber(GeneralizedValidatedVector gvviA, GeneralizedValidatedVector gvviB, int iEntropyNumberIndexA, int iEntropyNumberIndexB) throws java.lang.Exception
Compute the Upper Bound for the Entropy Number of the Operator Sample Metric Covering Number Convolution Product across both the Function Classes- Parameters:
gvviA
- The Validated Input Vector Space Instance for Class AgvviB
- The Validated Input Vector Space Instance for Class BiEntropyNumberIndexA
- Entropy Number Index for Class AiEntropyNumberIndexB
- Entropy Number Index for Class B- Returns:
- The Upper Bound for the Entropy Number of the Operator Sample Metric Covering Number Convolution Product across both the Function Classes
- Throws:
java.lang.Exception
- Thrown if the Convolution Product Sample Metric Entropy Number cannot be Computed
-
sampleSupremumEntropyNumber
public double sampleSupremumEntropyNumber(GeneralizedValidatedVector gvviA, GeneralizedValidatedVector gvviB, int iEntropyNumberIndexA, int iEntropyNumberIndexB) throws java.lang.Exception
Compute the Upper Bound for the Entropy Number of the Operator Sample Supremum Covering Number Convolution Product across both the Function Classes- Parameters:
gvviA
- The Validated Input Vector Space Instance for Class AgvviB
- The Validated Input Vector Space Instance for Class BiEntropyNumberIndexA
- Entropy Number Index for Class AiEntropyNumberIndexB
- Entropy Number Index for Class B- Returns:
- The Upper Bound for the Entropy Number of the Operator Sample Supremum Covering Number Convolution Product across both the Function Classes
- Throws:
java.lang.Exception
- Thrown if the Convolution Product Sample Supremum Entropy Number cannot be Computed
-
normedEntropyUpperBound
public CarlStephaniNormedBounds normedEntropyUpperBound(MaureyOperatorCoveringBounds mocbA, MaureyOperatorCoveringBounds mocbB, int iEntropyNumberIndex, boolean bUseSupremumNorm)
Compute the Normed Upper Entropy Convolution Product Bound across the Function Classes- Parameters:
mocbA
- The Maurey Operator Covering Bounds for Class AmocbB
- The Maurey Operator Covering Bounds for Class BiEntropyNumberIndex
- Entropy Number Index for either ClassbUseSupremumNorm
- TRUE/FALSE - Use the Supremum/Metric Bound as the Operator Function Class- Returns:
- The Normed Upper Entropy Convolution Product Bound across the Function Classes
-
populationSupremumEntropyNorm
public CarlStephaniNormedBounds populationSupremumEntropyNorm(int iEntropyNumberIndex, boolean bUseSupremumNorm)
Compute the Population Supremum Carl-Stephani Entropy Number Upper Bound using either the Metric/Supremum Population Norm- Parameters:
iEntropyNumberIndex
- Entropy Number Index for either ClassbUseSupremumNorm
- TRUE/FALSE - Use the Supremum/Metric Bound as the Operator Function Class- Returns:
- The Population Supremum Carl-Stephani Entropy Number Upper Bound using either the Metric/Supremum Population Norm
-
populationMetricEntropyNorm
public CarlStephaniNormedBounds populationMetricEntropyNorm(int iEntropyNumberIndex, boolean bUseSupremumNorm)
Compute the Population Metric Carl-Stephani Entropy Number Upper Bound using either the Metric/Supremum Population Norm- Parameters:
iEntropyNumberIndex
- Entropy Number Index for either ClassbUseSupremumNorm
- TRUE/FALSE - Use the Supremum/Metric Bound as the Operator Function Class- Returns:
- The Population Metric Carl-Stephani Entropy Number Upper Bound using either the Metric/Supremum Population Norm
-
sampleSupremumEntropyNorm
public CarlStephaniNormedBounds sampleSupremumEntropyNorm(GeneralizedValidatedVector gvviA, GeneralizedValidatedVector gvviB, int iEntropyNumberIndex, boolean bUseSupremumNorm)
Compute the Sample Supremum Carl-Stephani Entropy Number Upper Bound using either the Metric/Supremum Population Norm- Parameters:
gvviA
- The Validated Input Vector Space Instance for Class AgvviB
- The Validated Input Vector Space Instance for Class BiEntropyNumberIndex
- Entropy Number Index for either ClassbUseSupremumNorm
- TRUE/FALSE - Use the Supremum/Metric Bound as the Operator Function Class- Returns:
- The Sample Supremum Carl-Stephani Entropy Number Upper Bound using either the Metric/Supremum Population Norm
-
sampleMetricEntropyNorm
public CarlStephaniNormedBounds sampleMetricEntropyNorm(GeneralizedValidatedVector gvviA, GeneralizedValidatedVector gvviB, int iEntropyNumberIndex, boolean bUseSupremumNorm)
Compute the Sample Metric Carl-Stephani Entropy Number Upper Bound using either the Metric/Supremum Population Norm- Parameters:
gvviA
- The Validated Input Vector Space Instance for Class AgvviB
- The Validated Input Vector Space Instance for Class BiEntropyNumberIndex
- Entropy Number Index for either ClassbUseSupremumNorm
- TRUE/FALSE - Use the Supremum/Metric Bound as the Operator Function Class- Returns:
- The Sample Metric Carl-Stephani Entropy Number Upper Bound using either the Metric/Supremum Population Norm
-
-


{% extends "admin/base_site.html" %} {% block title %}Reviews Raffle{% endblock %} {% block content_title %}
Provide a range of dates and return one or more randomly selected reviewers. Each review submitted in the date range counts as a single raffle entry.
{% if winner_reviews %}


Help
Online Booking System
Status Of Facilities
Facility :
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20 Nanyang Green, Singapore 637715
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[gecode-users] argmin
Christian Schulte
cschulte at kth.se
Thu Jun 25 11:00:08 CEST 2015
Hi Kish,
Thanks for the points. I'll address all of them a little later (end of
semester rush here).
Cheers
Christian
--
Christian Schulte, www.gecode.org/~schulte
Professor of Computer Science, KTH, cschulte at kth.se
Expert Researcher, SICS, cschulte at sics.se
-----Original Message-----
From: users-bounces at gecode.org [mailto:users-bounces at gecode.org] On Behalf
Of Kish Shen
Sent: Wednesday, June 24, 2015 10:50 PM
To: users at gecode.org
Subject: Re: [gecode-users] argmin
Hi Christian,
Thanks for the pointer. I assume as applied here, it is the index for the
variable(s) in x that has the minimum value.
I did not know (or at least remember) about argmin, and I don't know if it
is common knowledge, so perhaps a little more explanation in the MPG might
be helpful?
I assume there is no offset that can be specified for this constraint, to
offset the index, as is available for some of the other constraints?
That is, in order to get this constraint so that the index start from 1, I
need to add a dummy 0'th element to x?
I think argmin is min_index in GCAT? This is not mentioned in the MPG, as is
done for other constraints in the MPG, or am I missing some differences
between argmin and min_index? (GCAT's minimum is also not mentioned for min)
Cheers,
Kish
On 24/06/2015 10:45, Christian Schulte wrote:
> Well it is just argmin:
> https://en.wikipedia.org/wiki/Arg_max
>
> Christian
>
> --
> Christian Schulte, www.gecode.org/~schulte Professor of Computer
> Science, KTH, cschulte at kth.se Expert Researcher, SICS,
> cschulte at sics.se
>
>
> -----Original Message-----
> From: users-bounces at gecode.org [mailto:users-bounces at gecode.org] On
> Behalf Of Kish Shen
> Sent: Wednesday, June 24, 2015 9:41 AM
> To: users at gecode.org
> Subject: [gecode-users] argmin
>
> Hi,
>
> I am trying to understand the argmin/max constraints, but I don't
> understand the description in the MPG (section 4.4.5 Arithemetic
> constraints),L what does Chargmin(x) mean in:
>
> argmin(home, x, y)
>
> constrains y to argimin(x)?
>
>
> Cheers,
>
> Kish
>
>
> _______________________________________________
> Gecode users mailing list
> users at gecode.org
> https://www.gecode.org/mailman/listinfo/gecode-users
>
_______________________________________________
Gecode users mailing list
users at gecode.org
https://www.gecode.org/mailman/listinfo/gecode-users
More information about the users
mailing list


Use this list to specify which line
separator is to be used in files created by IntelliJ IDEA. The available options are:
- System dependent:
choose this option to use the default selection.
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choose this option to use the Unix and macOS line separator.
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choose this option to use the Windows line separator.
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choose this option to use the Classic Mac line separator.
(Applied to new files.)


" Type error: Call needs function or class, got: undefined "then the object you are exporting (the body...not the bones) has spaces in its name. For example.... enemy_NPC will export... but enemy NPC will cause the above error. As far as I can tell... bone names with spaces are fine... but the actual mesh must NOT have spaces in it.


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Here are links to final projects and presentations from the Symposium Spectacular. Congratulations to all the teams for impressive work and engaging presentations!
UVA Students and COVID Spread
Maxwell Patek, Nate Strawser, Nicolas Izurrategui, Muxuan Yan
Pizza-Winning Presentation: Video
Project Site: http://people.virginia.edu/~my2pk/infection-simulator/sites/
Source Code: https://github.com/max7patek/infection-simulator
COVID-19 Interactive Simulator
Will Define, Shen Yan, Christian Paget, Brienne Tyler
Pizza-Winning Presentation: Video
Project Site: https://wdefine.github.io/mmmfinalproject/
Optimizing your COVID-19 Finances: A Survival Guide
Disha Jain, Sindhu Ranga, Jason Quinn, Sameer Gupta
Presentation: Video, Presentation Slides
NLP for COVID19
Eric Wang, Serena Jiao, Megan Cheng, Christine Cheng
Presentation: Video
Project Site:
https://github.com/ericjoshuawang/NLP_COVID19
Effect of Contact Tracing on the Spread of COVID-19
Rebecca Zhou, Avery Burton, Eimara Mirza, Louisa Evola
Presentation: Video
Project Site: https://sites.google.com/virginia.edu/contact-tracing/home
Risky Business | An Analysis of Health Risk Factors
Anna Davis, Courtney Jacobs, Sally Greenberg, Chris Puglisi
Project Site: https://mmms20-riskybusiness.ue.r.appspot.com/"
Income and Occupation Effects on Social Distancing
Ryan Keller, Zac Bilmen, Ravi Kodali, Jami Park
Presentation: Video
Project Site: https://github.com/zb3qk/Social-Distancing-By-Occupation
Inside China’s Two-Child Policy
Margaret Cheng, Sophia Cheung, Yilin Huang, Yutong Zhong
Presentation: Video
Project Site: https://yh3mw1.wixsite.com/mmm-2
Soccer Player Ratings
Joe Karaki, Jack Short
Presentation: Video
Hotel Occupancy Optimization
Sam Spreen, Arjun Iyer, Ashwin Krishnamurthi, Rohan Malladi
Presentation: Video
Project Report: PDF
COVID-19 Stock Exploration and Time Series Analysis
Faisal Refai, Misran Dolan, Hisham Iqbal
COVIM: COVID-19 Intervention Modeling
Kiara Gan, Aaron Ho, Rashid Lasker, Eli Lifland
Presentation: Video
Project Site: https://covim.herokuapp.com/
Analyzing the Impacts of Covid 19 Through Travel Data
Saeed Razavi, Sharvari Bhatt, Leon Smith, Yi Fan Lim, Lydia Kim
Project Site: https://saeedrazavi.github.io/MMMAnalyzing-Economic-Impacts-of-COVID-19-Through-Travel-Data/


What code should you always wrap your jQuery in?
$(document).ready(function(event){
// jQuery code goes in here
});
The .css() method can handle any CSS changes in jQuery.
$('h1').css('font-size', '30px');
$('#header').css({
height: '300px',
background: '#333'
});
The first line of code above shows you how to update a single CSS property, in this case we're updating the h1 tag and changing the font-size to 30px.
The second section shows you how to update multiple CSS properties at once.
Using the .css() method, update the CSS of an element.
One of the most commonly used events is the click event. The click event is a method that gets triggered whenever someone clicks on the HTML element that is declared at the beginning of the click event method.
HTML:
<button>Click here to reveal the text</button>

Your secret text here

CSS:
.superhero-weakness {
display: none;
}
Add the above code to your HTML
In the below jQuery, we are creating an event that will go off whenever someone clicks on a button HTML element.
$('button').click(function(event){
$('.superhero-weakness').css('display', 'block');
});
When they click on it, this method will go after the element with a CLASS of superhero-weakness and change it from display none to block.
Use the button click event to change the text color of an HTML element.
Go to http://www.bizstreamacademy.com and go to the Resources page.
At the bottom of the page, right click on the about.html and contact.html and choose 'Save link as...' in the dropdown. Then navigate to and save to your project folder.
Save those 2 files to your project folder.
Add the following HTML inside or below your header HTML:

Home

About

Contact

The above code is the basic structure for most website menus. It utilizes the unordered list HTML and wraps it in a nav HTML5 tag.
#menu {
float: right;
}
#menu ul {
list-style: none;
margin: 0;
padding: 0;
}
#menu ul li {
float: left;
}
#menu ul li a {
display: block;
font-size: 20px;
padding-left: 35px;
line-height: 65px;
}
Our above CSS does 2 main things. First, it takes away the 'list' formatting that comes with an unordered list HTML element. Then it takes the li's and 'floats' them to the left (so they're all next to each other).
Take the next 5 minutes to work on your about or contact page. Or ask us any questions about how to edit the menu!
A media query is a way of specifying certain CSS at different screen widths.
@media only screen and (max-width: 750px) {
body {
background-color: blue;
}
}
The above code states "If the browser/device window is smaller than 750px, then the background color will change to blue."
To keep things simple, we'll be separating out our mobile styles in a new stylesheet.
In your HTML head, below where your style.css is declared, paste in this HTML:
<link href="mobile.css" type="text/css" rel="stylesheet">
Then create a new file called mobile.css in your project folder.
In your HTML head, preferably at the top, but it can go anywhere in your head section.
We need this meta tag to optimize your website for mobile devices.
<meta name="viewport" content="width=device-width, initial-scale=1">
In your newly created mobile.css file, add the following css:
@media only screen and (max-width: 750px) {
body {
background-color: blue;
}
}
Resize your browser to see how your background color changes on a small screen.
Inside your mobile.css file, add the following CSS:
#menu ul li {
float: none;
}
#menu ul li a {
padding: 0;
line-height: 30px;
text-align: right;
}
We're adding this CSS because we want the menu items to appear 'stacked' in our mobile view and not all right next to each other.
A hamburger menu is simply 3 bars in a row from top to bottom. It is an icon that has become known to show a site's menu when you click on it.
Go to http://www.bizstreamacademy.com and go to the Resources page.
At the bottom of the page, right click on the menu-icon-white.png OR menu-icon-black.png. If you have a dark colored background, choose white, otherwise choose black. Then from the dropdown chooose "Save link as..." and navigate to your project folder.
Add the following code to your HTML, above your menu HTML

The img tag here is your hamburger menu you downloaded. Then we wrap that in a div with a class of toggle-menu. We'll use that class in a minute to active our mobile menu.
Right now the hamburger menu is always there, but we really only want to see it on mobile.
Add the following CSS to your style.css file.
.toggle-menu {
display: none;
}
Now that the mobile menu icon is hidden on desktop, we need to display it on mobile and hide the menu itself.
Add the following CSS to your mobile.css file:
.toggle-menu {
display: block;
float: right;
margin-top: 30px;
}
#menu {
display: none;
}
Desktop = menu shows, hamburger menu icon hidden
Mobile = menu hidden, hamburger menu icon shows
Any guesses?
The following code will execute when you click on the div with class toggle-menu. Then it will toggle the menu on and off.
$('.toggle-menu').click(function() {
$('#menu').slideToggle();
});
The slideToggle command in jQuery is used to turn the display of an HTML element on and off.
Want to style your mobile menu more? Or style something else for your mobile view? Maybe change a column width to 100%?


Namespace HarmonyLib
Classes
AccessTools
A helper class for reflection related functions
Code
CodeInstruction
An abstract wrapper around OpCode and their operands. Used by transpilers
CodeInstructionExtensions
Extensions for CodeInstruction
CodeInstructionsExtensions
Extensions for a sequence of CodeInstruction
CodeMatch
A CodeInstruction match
CodeMatcher
A CodeInstruction matcher
CollectionExtensions
General extensions for collections
DelegateTypeFactory
A factory to create delegate types
ExceptionBlock
An exception block
FastAccess
A helper class for fast access to getters and setters
FileLog
A file log for debugging
GeneralExtensions
General extensions for common cases
Harmony
The Harmony instance is the main entry to Harmony. After creating one with an unique identifier, it is used to patch and query the current application domain
HarmonyAfter
A Harmony annotation
HarmonyArgument
A Harmony annotation
HarmonyAttribute
The base class for all Harmony annotations (not meant to be used directly)
HarmonyBefore
A Harmony annotation
HarmonyCleanup
Specifies the Cleanup function in a patch class
HarmonyDebug
A Harmony annotation
HarmonyDelegate
Annotation to define the original method for delegate injection
HarmonyException
Under Mono, HarmonyException wraps IL compile errors with detailed information about the failure
HarmonyFinalizer
Specifies the Finalizer function in a patch class
HarmonyMethod
A wrapper around a method to use it as a patch (for example a Prefix)
HarmonyMethodExtensions
Annotation extensions
HarmonyPatch
Annotation to define your Harmony patch methods
HarmonyPatchAll
A Harmony annotation to define that all methods in a class are to be patched
HarmonyPatchCategory
Annotation to define a category for use with PatchCategory
HarmonyPostfix
Specifies the Postfix function in a patch class
HarmonyPrefix
Specifies the Prefix function in a patch class
HarmonyPrepare
Specifies the Prepare function in a patch class
HarmonyPriority
A Harmony annotation
HarmonyReversePatch
Annotation to define your standin methods for reverse patching
HarmonyTargetMethod
Specifies the TargetMethod function in a patch class
HarmonyTargetMethods
Specifies the TargetMethods function in a patch class
HarmonyTranspiler
Specifies the Transpiler function in a patch class
MethodBaseExtensions
General extensions for collections
MethodInvoker
A helper class to invoke method with delegates
Patch
A serializable patch
PatchClassProcessor
A PatchClassProcessor used to turn HarmonyAttribute on a class/type into patches
Patches
A group of patches
PatchInfo
Serializable patch information
PatchProcessor
A PatchProcessor handles patches on a method/constructor
Priority
A patch priority
ReversePatcher
A reverse patcher
SymbolExtensions
A helper class to retrieve reflection info for non-private methods
Transpilers
A collection of commonly used transpilers
Traverse
A reflection helper to read and write private elements
Traverse<T>
A reflection helper to read and write private elements
Enums
ArgumentType
Specifies the type of argument
ExceptionBlockType
Exception block types
HarmonyPatchType
Specifies the type of patch
HarmonyReversePatchType
Specifies the type of reverse patch
MethodDispatchType
Specifies the type of method call dispatching mechanics
MethodType
Specifies the type of method
Delegates
AccessTools.FieldRef<F>
A readable/assignable reference delegate to a static field
AccessTools.FieldRef<T, F>
A readable/assignable reference delegate to an instance field of a class or static field (NOT an instance field of a struct)
AccessTools.StructFieldRef<T, F>
A readable/assignable reference delegate to an instance field of a struct
FastInvokeHandler
A delegate to invoke a method
GetterHandler<T, S>
A getter delegate type
InstantiationHandler<T>
A constructor delegate type
SetterHandler<T, S>
A setter delegate type


Alpha
Revive
Microblogging for App.net
This instance of Alpha is currently in development.
This isn't a clone of App.net itself, but is a microblogging client for it.
Feature requests can be sent to<EMAIL_ADDRESS>{% extends 'pau/base.html' %} {% block nav %}{% endblock %} {% block hero_block %}
Microblogging for App.net
This instance of Alpha is currently in development.
This isn't a clone of App.net itself, but is a microblogging client for it.
Feature requests can be sent to alpha@a.jvimedia.org


| global print and cat {pbdMPI} | R Documentation |
The functions globally print or cat a variable from specified processors, by default messages is shown on screen.
comm.print(x, all.rank = .pbd_env$SPMD.CT$print.all.rank, rank.print = .pbd_env$SPMD.CT$rank.source, comm = .pbd_env$SPMD.CT$comm, quiet = .pbd_env$SPMD.CT$print.quiet, flush = .pbd_env$SPMD.CT$msg.flush, barrier = .pbd_env$SPMD.CT$msg.barrier, con = stdout(), ...) comm.cat(..., all.rank = .pbd_env$SPMD.CT$print.all.rank, rank.print = .pbd_env$SPMD.CT$rank.source, comm = .pbd_env$SPMD.CT$comm, quiet = .pbd_env$SPMD.CT$print.quiet, sep = " ", fill = FALSE, labels = NULL, append = FALSE, flush = .pbd_env$SPMD.CT$msg.flush, barrier = .pbd_env$SPMD.CT$msg.barrier, con = stdout())
x |
a variable to be printed. |
... |
variables to be cat. |
all.rank |
if all ranks print (default = FALSE). |
rank.print |
rank for printing if not all ranks print (default = 0). |
comm |
communicator for printing (default = 1). |
quiet |
FALSE for printing rank number. |
sep |
sep argument as in the |
fill |
fill argument as in the |
labels |
labels argument as in the |
append |
labels argument as in the |
flush |
if flush |
barrier |
if barrier |
con |
|
Warning: These two functions use barrier()
to make sure
the well printing process on screen, so should be called by all processors
to avoid a deadlock. A typical misuse is called inside a condition check,
such as if(.comm.rank == 0) comm.cat(...)
.
rank.print
can be a integer vector containing the ranks of
processors which print messages.
A print()
or cat()
is called for the specified processors
and the messages of the input variables is shown on screen by default.
Wei-Chen Chen<EMAIL_ADDRESS>George Ostrouchov, Drew Schmidt, Pragneshkumar Patel, and Hao Yu.
Programming with Big Data in R Website: http://r-pbd.org/
## Not run: ### Save code in a file "demo.r" and run with 2 processors by ### SHELL> mpiexec -np 2 Rscript demo.r ### Initial. suppressMessages(library(pbdMPI, quietly = TRUE)) init() ### Example. comm.print(comm.rank(), rank.print = 1) ### Finish. finalize() ## End(Not run)


Comments
-
Greg Hurrell
Status changed:
- From: new
- To: open
-
Greg Hurrell
Can you give me a complete example path for one of the plist files that appears in the log?
-
Greg Hurrell
Summary changed:
- From: Synergy
- To: CFPropertyListCreateFromXMLData() "plist parse failed" messages in console
-
anonymous
Created , edited
Here's a few of the most recent console log entries. Each entry is followed by a version check failure. Perhaps connected in some way ? Curious that the "suggested" file name is not always the same. A google search of "CFPropertyListCreateFromXMLData(): plist parse failed; the data is not proper UTF-8." produces many hits.
2009-05-27 03:31:17.442 Synergy[339] CFLog (0): CFPropertyListCreateFromXMLData(): plist parse failed; the data is not proper UTF-8. The file name for this data could be: /Users/foo/Library/Autosave Information/org.wincent.Synergy.plist The parser will retry as in 10.2, but the problem should be corrected in the plist. 2009-05-27 03:31:17.448 Synergy[339] Error while attempting automatic version check 2009-05-27 21:00:47.339 Synergy[228] CFLog (0): CFPropertyListCreateFromXMLData(): plist parse failed; the data is not proper UTF-8. The file name for this data could be: /Users/foo/Library/Cookies/Cookies.plist The parser will retry as in 10.2, but the problem should be corrected in the plist. 2009-05-27 21:00:47.345 Synergy[228] Error while attempting automatic version check 2009-06-01 06:27:29.820 Synergy[228] CFLog (0): CFPropertyListCreateFromXMLData(): plist parse failed; the data is not proper UTF-8. The file name for this data could be: /Users/foo/Library/Autosave Information/org.wincent.Synergy.plist The parser will retry as in 10.2, but the problem should be corrected in the plist. 2009-06-01 06:27:29.826 Synergy[228] Error while attempting automatic version check 2009-06-02 06:27:29.876 Synergy[228] CFLog (0): CFPropertyListCreateFromXMLData(): plist parse failed; the data is not proper UTF-8. The file name for this data could be: /Users/foo/Library/Autosave Information/org.wincent.Synergy.plist The parser will retry as in 10.2, but the problem should be corrected in the plist. 2009-06-02 06:27:29.883 Synergy[228] Error while attempting automatic version check 2009-06-03 00:16:28.218 Synergy[293] CFLog (0): CFPropertyListCreateFromXMLData(): plist parse failed; the data is not proper UTF-8. The file name for this data could be: /Users/foo/Library/Cookies/Cookies.plist The parser will retry as in 10.2, but the problem should be corrected in the plist. 2009-06-03 00:16:28.223 Synergy[293] Error while attempting automatic version check 2009-06-05 09:13:22.290 Synergy[562] CFLog (0): CFPropertyListCreateFromXMLData(): plist parse failed; the data is not proper UTF-8. The file name for this data could be: /Users/foo/Library/Cookies/Cookies.plist The parser will retry as in 10.2, but the problem should be corrected in the plist. 2009-06-05 09:13:22.301 Synergy[562] Error while attempting automatic version check 2009-06-06 09:13:22.391 Synergy[562] CFLog (0): CFPropertyListCreateFromXMLData(): plist parse failed; the data is not proper UTF-8. The file name for this data could be: /Users/foo/Library/Cookies/Cookies.plist The parser will retry as in 10.2, but the problem should be corrected in the plist. 2009-06-06 09:13:22.402 Synergy[562] Error while attempting automatic version check
-
Greg Hurrell
Interesting. Both of the paths mentioned — the "autosave" one and the "cookies" one — are managed by the system and are never explicitly manipulated by Synergy itself.
Can I suggest quitting Synergy, deleting both of those files, and seeing what happens?
-
anonymous
Even stranger:
The "Autosave Information" file does not exist. Did it ever ? I wonder "who" makes it ? Since 6 June no further console messages.
I'll delete the "Cookies" file, relaunch as suggested, and report back one way or the other. After an appropriate elapse of time, reboot, etc.
-
anonymous
Created , edited
Well, didn't take too long. Console message reappeared :-)
Before deleting file and restarting Synergy:
2009-06-09 21:02:42.788 Synergy[562] CFLog (0): CFPropertyListCreateFromXMLData(): plist parse failed; the data is not proper UTF-8. The file name for this data could be: /Users/foo/Library/Application Support/Synergy/Album Covers/Artist-BBCRadio4,Album-ReithLectures2009.plist The parser will retry as in 10.2, but the problem should be corrected in the plist. 2009-06-09 21:02:42.795 Synergy[562] Error while attempting automatic version check And first time we restart Synergy: 2009-06-09 22:32:38.066 Synergy[5023] CFLog (0): CFPropertyListCreateFromXMLData(): plist parse failed; the data is not proper UTF-8. The file name for this data could be: /Users/foo/Library/Autosave Information/org.wincent.Synergy.plist The parser will retry as in 10.2, but the problem should be corrected in the plist. 2009-06-09 22:32:38.073 Synergy[5023] Error while attempting automatic version check Restart Synergy again: 2009-06-09 22:34:55.232 Synergy[5028] CFLog (0): CFPropertyListCreateFromXMLData(): plist parse failed; the data is not proper UTF-8. The file name for this data could be: /Users/foo/Library/Autosave Information/org.wincent.Synergy.plist The parser will retry as in 10.2, but the problem should be corrected in the plist. 2009-06-09 22:34:55.239 Synergy[5028] Error while attempting automatic version check
Something odd. Possibly the file name diagnostic is simply an unhelpful red herring. Many google hits, here's one at randomish:
-
Greg Hurrell
Ok, the autosave information I have no idea how or why that one gets created. There are a lot of places in Mac OS X where you request a resource or perform an operation and the system itself will create this kind of settings file, or cache, or whatever, and will supposedly clean up after itself. Undoubtedly it's being triggered indirectly by use of some other API. I might be able to set a breakpoint on
CFLog()
and find out where it's originating from.As for the other messages, eg the
/Users/foo/Library/Application Support/Synergy/Album Covers/Artist-BBCRadio4,Album-ReithLectures2009.plist
; those are specifically created by Synergy, so I will look at the place where that's happening and see if I can switch to a different API which doesn't trigger those messages.Needless to say, you shouldn't worry too much about these messages as they're not actually indicative of any real problem. But I will see if I can remove them as a cosmetic clean up anyway.
-
anonymous
Thanks - I wasn't (worrying) :)
I did wonder if they might in some way be related to the version check message (it is consistently close in time). Unless/until you release a new version I shall not know whether the version check is actually working as intended. I do recall not becoming aware that there was a version 3.2.2 available until I checked the web-site for some reason, so perhaps there is a real defect.
Regards
-
Greg Hurrell
Ok, will look into the version check stuff as well to make sure it's all working as intended.
Still don't have proper Internet access yet, but the technicians are apparently coming tomorrow so should be able to make some progress soon.
Add a comment
Comments are now closed for this issue.


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Chapter 2 Bayesian - 2
2.1 Components of Bayesian models
\[y_i=\mu+\epsilon_i\]
Where,
\[\epsilon_i \sim N(0, \sigma^2)\]
\[y_i \sim N(\mu, \sigma^2)\] (Thus, \(y_i\) is \(=\) to a fixed \(\mu\) plus with a \(\epsilon_i\), whereas \(y_i \sim N(\mu, \sigma^2)\). These two expressions are not exactly the same, but they are connected.)
Likelihood: \(P(y|\theta)\) (\(P(y,\theta)=P(\theta)P(y|\theta)\))
Prior: \(P(\theta)\)
Posterior:
\[P(\theta|y)=\frac{P(\theta, y)}{P(y)}=\frac{P(\theta, y)}{\int P(\theta, y)d\theta}=\frac{P(\theta)P(y|\theta)}{\int P(\theta, y)d\theta}=\frac{P(\theta)P(y|\theta)}{\int P(\theta)P(y|\theta)d\theta}\] Markts
The only random variables in frequentist models are the data. In contrast, Bayesian paradigm also uses probability to describe one’s uncertainty about unknown model parameters.
Consider the following model for binary outcome \(y\):
\(y_i|\theta_i \sim Bern (\theta_i), i=1,2,3...6\)
\(\theta_i |\alpha \sim Beta(a, b_0), i=1,2,3...6\)
\(\alpha \sim Exp(r_0)\)
Thus, the joint distribution of all variable:
\[\prod_{i=1}^{6}[\theta_i^{y_i}(1-\theta_i)^{1-y_i}\frac{\Gamma(a+b_0)}{\Gamma(a)\Gamma(b_0)}\theta_i^{a-1}(1-\theta_i)^{b_0-1}]r_0e^{-r_0 \alpha}\] (Question: Why not write it as \(a_i\)?)
2.2 Monte Carlo Estimation
2.2.1 Mean and Variance: Application of Central Limit Theorem
If we simulate 100 samples from a Gamma(2,1), what is the approximate distribution of the sample average \(\bar{x^*}=\frac{1}{100} \sum_{i=1}^{100} x_i^*\)?
As we know, based on the central limit theorem, the approximating distribution is normal with the mean equal to the sample mean, and the variance equal to the variance of the orignal variable divided by the sample size. We know that the mean for Gamma(2,1) is \(\frac{2}{1}=2\) and variance is \(\frac{2}{1^2}=2\). Thus, we know that the mean for the distribution for \(\bar{x^*}\) is 2, whereas the variance is \(\frac{2}{100}\). Thus, it is \(N(2,0.02)\).
Thus, we can get the generalized formula as follows:
\[\bar{\theta^*} \sim N(E(\theta), \frac{Var(\theta)}{m})\] Note that, it is the variance for \(\bar{\theta^*}\), not \(\theta\). The approximate for the variance for \(\theta\) is \(\frac{1}{m}\sum(\theta_i^*-\bar{\theta^*})^2\).
The following R code is for the \(\theta\):
## [1] 2.005936
## [1] 0.9919974
The following R code is for the \(\bar{\theta^*}\). As we can see, the variance is 0.0016, which is close to the true value of \(\frac{1}{1000}=0.001\).
## [1] 0.001634891
Side Note
Note that the definition of variance is:
\[V(x)=E[(x-\mu)^2]\]
Thus, we can calculate the variance using integral:
\[V(x)=\int (x-\mu)^2 f(x)dx\] When we using samples from the simulation, we will get the following:
\[V(x)=\int (x-\mu)^2 f(x)dx= \frac{1}{m} \sum (x_i^*-\bar{x^*})^2\] Thus, we can use \(Var\) function in \(R\) with simulated sample to calculate the variance.
2.2.2 Monte Carlo error (Standard Error)
For the \(\bar{x^*}\), we can use the CLT to approximate how accurate the Monte Carlo estimates are.
\[\frac{SD(sample)}{\sqrt{m}}\]
set.seed(123)
sample_data_gamma<-rgamma(10000,4,2)
hist(sample_data_gamma, freq=FALSE)
curve(dgamma(x=x, shape=4, rate=2), col="blue", add=TRUE)
## [1] 0.9682145
## [1] 0.9839789
## [1] 0.9839789
## [1] 0.009839789
We can also calculate Standard Error for the probablity
## [1] 0.0009547917
2.2.3 Expected value and probability
If you know that \(\theta \sim Beta(5,3)\), what is the approximate for the \(E(\frac{\theta}{1-\theta})\)? You can use the following R code to calculate it.
## [1] 2.789685
If you want to calculate the approximate for the probability that \(\frac{\theta}{1-\theta}\) is greater 1.
## [1] 0.789
2.2.4 Quantile
Use Monte Carlo to approximate the 0.3 quantile of N(0,1). Note that, the idea of quantile is to quantify the probability. Thus, the number we will get for 0.3 quantile if the value for the random variable’s cdf \(\int_{-\infty}^{quantile-number}dx\). As we can see below, \(quantile(sample_data2,0.3)\) gets the result of -0.52, which is the same from the \(qnorm(0.3)\). Note that, the cdf of \(pnorm(-0.52)\) will get the probablity of \(0.3\).
## 30%
## -0.5260847
## [1] -0.5244005
## [1] 0.3015318
2.2.5 Prior predictive distributions (Marginalization)
\[y|\theta \sim Bin(10, \theta)\] \[\theta \sim Beta(2,2)\]
Simulate:
\(\theta^*\) from beta
Given \(\theta\), draw \(y_i^* \sim Bin(10, \theta_i^*)\)
Get pairs (\(y_i^*\),\(\theta_i^*\))
m=1000
y=numeric(m)
phi=numeric(m)
for (i in 1:m)
{
phi[i]=rbeta(1,shape1=2,shape2 = 2)
y[i]=rbinom(1,size=10,prob = phi[i])
}
# we can use vector method
phi=rbeta(m,shape1 = 2,shape2 = 2)
y=rbinom(m,size=10,prob = phi)
table(y)/m
## y
## 0 1 2 3 4 5 6 7 8 9 10
## 0.038 0.064 0.100 0.110 0.124 0.112 0.131 0.117 0.093 0.069 0.042
## [1] 5.04
2.3 Markov chains
2.3.1 Definition
A sequence of random variable \(X_1, X_2,...,X_n\), with \(1, 2, ...,3\) indicating the successitve points in time. Thus, based on the chain rule, we can write the following:
\[p(X_1,X_2,...,X_n)=p(X_1)p(X_2|X_1)p(X_3|X_2,X_1)...p(X_n|X_{n-1},X_{n-2},...,X_2,X_1)\]
For Markov chains, it puts an assumption, called Markov assumption: The random variable at the next time step only depends on the current variable. Mathematically,
\[p(X_{t+1}|X_t,X_{t-1},...,X_2,X_1)=p(X_{t+1}|X_t)\]
where,
\[t=2, ...n\]
Thus, we can write the expression above as follows.
\[p(X_1,X_2,...,X_n)=p(X_1)p(X_2|X_1)p(X_3|X_2)P(X_4|X_3)...p(X_n|X_{n-1})\]
2.3.2 Discrete example
Suppose that you flip a coin. You have a set of number {1,2,3,4,5}. If it is head, you increase 1 in the next number (for instance, if you are 2 now, you will be get 3 in the next). In contrast, if it is tail, you will decrease the number (e.g., 2 is now and 1 is next). If is is 5, increase 1 will lead to 1. Logically, 1 and then it is reduced by 1, leading to 5.
2.3.3 Continuous example
\[p(X_{t+1}| X_t=x_t)=N(x_t,1)\]
2.3.4 Discrete example and transition matrix
For the discrete example above, we can write the following transition matrix. We know that \(p(X_{t+1}=5 | X_t=4)\) can be found in the 4th row, 5th column, namely 0.5.
\[Q=\begin{bmatrix} 0 & 0.5 & 0 & 0 & 0.5 \\ 0.5 & 0 & 0.5 & 0 & 0\\ 0 & 0.5 & 0 & 0.5 & 0 \\ 0& 0 & 0.5& 0& 0.5\\ 0.5 & 0& 0 &0.5 & 0 \end{bmatrix}\]
The transition matrix is especially useful when there are mutiple steps in the chain. such as \(p(X_{t+1}=5| X_t=4)\). It can be calculated as \(\sum_{k=1}^{5} p(X_{t+2}=3|X_{t+1}=k) \cdot p(X_{t+1}=k |X_t=1)\). We can use R code the impletement this.

Q = matrix(c(0.0, 0.5, 0.0, 0.0, 0.5,
0.5, 0.0, 0.5, 0.0, 0.0,
0.0, 0.5, 0.0, 0.5, 0.0,
0.0, 0.0, 0.5, 0.0, 0.5,
0.5, 0.0, 0.0, 0.5, 0.0),
nrow=5, byrow=TRUE)
Q
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.0 0.5 0.0 0.0 0.5
## [2,] 0.5 0.0 0.5 0.0 0.0
## [3,] 0.0 0.5 0.0 0.5 0.0
## [4,] 0.0 0.0 0.5 0.0 0.5
## [5,] 0.5 0.0 0.0 0.5 0.0
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.2 0.2 0.2 0.2 0.2
The definition of stationary distribution of a chain:
The initial state distribution for which performing a transition will not change the probability of ending up in any given state.
For a Markov chain after many iterations, the samples can be used as a Monte Carlo sample from the stationary distribution. Thus, we can use Markov chains for Bayesian inference. In order to simulate from a complicated posterior distribution, we will set up and run a Markov chain whose stationary distribution is the posterior distribution. (Note that: stationary distribution doesn’t always exist for any given Markov chain)
Continuous Cases_
As we can see, the example shown earlier does not reach a stationary distribution. But, we can modify it to make it happen.
\[p(X_{t+1}|X_t=x_t)=N(\phi x_t,1), -1<\phi<1\]
It will reach a stationary distribution as long as \(-1<\phi<1\).
n = 1000
x = numeric(n)
phi = -0.5
for (i in 2:n) {
x[i] = rnorm(1, mean=phi*x[i-1], sd=1.0)
}
plot.ts(x)
hist(x, freq=FALSE)
curve(dnorm(x, mean=0.0, sd=sqrt(1.0/(1.0-phi^2))), col="red", add=TRUE)
legend("topright", legend="theoretical stationary\ndistribution", col="red", lty=1, bty="n")
It will reach the stationary distribution of \(N(0, \frac{1}{1-\phi^2})\)
2.4 Metropolis-Hastings
2.4.1 Procedure
Select initial value \(\theta_0\)
For \(i=1,..., m\), repeat:
Draw candidate \(\theta^* \sim q(\theta^*|\theta_{i-1})\)
\(\alpha=\frac{g(\theta^*)/q(\theta^*|\theta_{i-1})}{g(\theta_{i-1})/q(\theta_{i-1}|\theta^*)}=\frac{g(\theta^*)q(\theta_{i-1}|\theta^*)}{g(\theta_{i-1})q(\theta^*|\theta_{i-1})}\)
\(\alpha \geq 1\) accept \(\theta^*\) and set \(\theta_i \leftarrow \theta^*\)
\(0<\alpha < 1\) accept \(\theta^*\) and set \(\theta_i \leftarrow \theta^*\) with probablity \(\alpha\)
2.4.2 Demonstration
Prior \(P(loaded)=0.6\)
\[\begin{aligned} f(\theta | X) &= \frac{f(x|\theta) f(\theta)}{\sum_{\theta} f(x|\theta)f(\theta)} \\ &=\frac{\binom{5}{x} [(\frac{1}{2})^5 \times 0.4 \times I_{\{\theta=fair \}}+ (0.7)^x(0.3)^{5-x} \times 0.6 \times I_{\{\theta=loaded \}}]}{\binom{5}{x} [(\frac{1}{2})^5 \times 0.4 + (0.7)^x(0.3)^{5-x} \times 0.6]} \end{aligned}\]
\[\begin{aligned} f(\theta |X=2) &=\frac{0.0125 I_{\{\theta=fair \}}+0.0079 I_{\{\theta=loaded \}} }{0.0125+0.0079} \\ &= 0.612 I_{\{\theta=fair \}} + 0.388 I_{\{\theta=loaded \}} \end{aligned}\] Thus, we can say that:
\[P(\theta=loaded | X=2)=0.388\]
Start at either \(\theta_0=fair\) or \(\theta_0=loaded\)
For \(i=1, ...m\)
Propose candidate \(\theta^*\) to be the other state such as \(\theta_{i-1}\)
\[\alpha=\frac{g(\theta^*)/q(\theta^*|\theta_{i-1})}{g(\theta_{i-1})/q(\theta_{i-1}|\theta^*)}=\frac{f(x=2|\theta^*)/1}{f(x=2|\theta_{i-1})/1}\]
If \(\theta^*=loaded, \alpha=\frac{0.0794}{0.0125}=0.635\) If \(\theta^*=fair, \alpha=\frac{0.0125}{0.0794}=1.574\)
- if \(\theta^*=fair, \alpha>1\), accept \(\theta^*\) set \(\theta_i=fair\)
if \(\theta^*=loaded, \alpha>0.635\), accept \(\theta^*\) set \(\theta_i=loaded\) w.p. 0.635.
Thus, we can get the following transition matrix:
\[Q=\begin{bmatrix} 0.365 & 0.635 \\ 1 & 0 \end{bmatrix}\]
This means that:
When \(\theta_{i-1}\) is fair, \(\theta_{i}\) is also fair, the \(P=0.365\).
When \(\theta_{i-1}\) is fair, \(\theta_{i}\) is loaded, the \(P=0.635\).
When \(\theta_{i-1}\) is loaded, \(\theta_{i}\) is fair, the \(P=1\).
When \(\theta_{i-1}\) is loaded, \(\theta_{i}\) is loaded, the \(P=0\).
Thus,
\[\pi = [0.612, 0.388]\]
\[[0.612, 0.388]\begin{bmatrix} 0.365 & 0.635 \\ 1 & 0 \end{bmatrix}=[0.612, 0.388]\]
2.4.3 R code
The data are the percent change in total personnel for n=10 companies (1.2,1.4,-0.5,0.3,0.9,2.3,1.0,0.1,1.3,1.9). We assume such changes follow a normal distribution with known variance (but mean is unknown). For the mean (i.e., \(\mu\)), we assume it follows a t distribution (i.e., the prior for the \(\mu\) is t-distribution).Since t-distribution prior is not conjugate to a normal distribution, the posterior distribution is not in a standard form that we can easily sample. Thus, we need to set up a Markov chain whose stationary distribution is the posterior distribution.
\[p(\mu|y_1,...,y_n) \propto \frac{exp[n(\bar{y}\mu-\mu^2/2)]}{1+\mu^2}\]
\[log(g(\mu))=n(\bar{y}\mu-\mu^2/2)-log(1+\mu^2)\]
lg=function(mu, n, ybar)
{ mu2=mu^2
n*(ybar*mu-mu2/2)-log(1+mu2)}
mu_now=10
cand_sd=5
y=c(1.2,1.4,-0.5,0.3,0.9,2.3,1.0,0.1,1.3,1.9)
ybar=mean(y)
m=1000
n=length(y)
mu_storage<-c()
mu_storage<-c(mu_storage,mu_now)
acceptance_count=0
mh<-function(mu_now,type_MH,cand_sd)
{
for (i in 1:m)
{
if (type_MH=="randomwalk") # random walk, as mean is updated with mu_now
{
mu_cand = rnorm(n=1, mean=mu_now, sd=cand_sd)
}
else
{
if(type_MH=="independent") # independent, as mean is fixed
{mu_cand = rnorm(n=1, mean=3, sd=cand_sd)}
else
{print("woring type of MH")
break
}
}
lg_alpha=lg(mu_cand,n=n,ybar = ybar)-lg(mu_now,n=n,ybar=ybar)
alpha=exp(lg_alpha)
if(alpha>1)
{mu_now=mu_cand
mu_storage<-c(mu_storage,mu_cand)
acceptance_count=acceptance_count+1}
else
{random_p=runif(1)
if(alpha>random_p)
{mu_now=mu_cand
acceptance_count=acceptance_count+1}
else
{mu_now=mu_now}
}
}
#return the following
list(mu=mu_storage,accpt=acceptance_count/m)
}
tt_0<-mh(mu_now=5,type_MH="randomwalk",cand_sd=10)
plot.ts(tt_0$mu)
## [1] 0.039
## [1] 0.931
## [1] 0.587
As we can see, when the variance is big, the acceptance rate is low. When the variance is low, in conrast, the acceptance rate is high. Note that, changing the mean does not change the acceptance rate too much. In the following, I plotted the prior, posteior, and the sample(i.e., \(y\)).
mu_keep<-tt_2$mu[-c(1:2)]
plot(density(mu_keep),xlim=c(-3,6))
curve(dt(x,df=1),lty=2,add = TRUE,col="red")
legend("topright",legend=c("Prior t-distribution pdf", "Posterior pdf"), col=c("red","black"), lty=1, bty="n")
points(ybar,0,pch=20)
legend(1,1, legend="Black dot is the sample mean (y_hat)",bty="n")
In the following, I show the situation that the proposal distribution is not random walk, but rather independent. That is, the mean for the normal distribution is fixed. Refer to the procedure of MH, there is a setence of:
Draw candidate \(\theta^* \sim q(\theta^*|\theta_{i-1})\)
## [1] 0.016
2.5 Gibbs sampling
2.5.1 Basic idea
\[p(\theta, \phi|y) \propto g(\theta, \phi)\]
\[p(\theta, \phi|y) = p(\phi |y)p(\theta| \phi,y)\]
Notes
\(p(\theta| \phi,y)\) is called the full conditional for theta.
We can update one parameter at a time.
\[p(\theta| \phi,y) \propto p(\theta, \phi|y) \propto g(\theta, \phi)\]
\[p(\phi| \theta,y) \propto p(\theta, \phi|y) \propto g(\theta, \phi)\]
2.5.2 Procedure
Initialize \(\theta_0\), \(\phi_0\).
For \(i=1,..., m\), repeat:
- Using \(\phi_{i-1}\), draw \(\theta_i \sim p(\theta|\phi_{i-1},y)\)
- Using \(\theta_i\), draw \(\phi_i \sim p(\phi|\theta_i,y)\)
2.5.3 Example
\[y_i|\mu, \sigma^2 \sim N(\mu, \sigma^2),i = 1, ...,n\]
\[\mu \sim N(\mu_0,\sigma_0^2)\]
\[\sigma^2 \sim IG(v_0,\beta_0)\]
\[\begin{aligned} p(\mu, \sigma^2) &\propto p(y1,...,y_n| \mu, \sigma^2)p(\mu)p(\sigma^2) \\ &=\prod_{i=1}^n[N(y_i|\mu,\sigma^2)]N(\mu|\mu_0,\sigma_0^2)IG(\sigma^2|v_0,\beta_0)\\ &= \prod_{i=1}^n [\frac{1}{\sqrt{2\pi \sigma^2}} exp(-\frac{1}{2\sigma^2}(y_i-\mu)^2)] \cdot \frac{1}{\sqrt{2\pi \sigma_0^2}} exp(-\frac{1}{2\sigma_0^2}(\mu-\mu_0)^2) \\ &\cdot \frac{\beta_0^{v_0}}{\Gamma(v_0)}(\sigma^2)^{-(v_0+1)}exp(-\frac{\beta_0}{\sigma^2}) \\ &\propto (\sigma^2)^{-\frac{n}{2}}exp[-\frac{1}{2\sigma^2} \sum_{i=1}^n(y_i-\mu)^2] \cdot exp[-\frac{1}{2\sigma_0^2}(\mu-\mu_0)^2] \cdot (\sigma^2)^{-(v_0+1)}exp[-\frac{\beta_0}{\sigma^2}] \end{aligned}\]
The following the full conditoinal distribution:
\[\begin{aligned} p(\mu| \sigma^2, y_1,...,y_n) &\propto p(\mu,\sigma^2 | y_1,...,y_n)\\ &\propto exp(-\frac{1}{2\sigma^2}(y_i-\mu)^2) exp[-\frac{1}{2\sigma_0^2}(\mu-\mu_0)^2] \\ &=exp[-\frac{1}{2\sigma^2}(y_i-\mu)^2-\frac{1}{2\sigma_0^2}(\mu-\mu_0)^2] \\ &\propto N(\mu |\frac{n\bar{y}/\sigma^2+\mu_0/\sigma^2}{n/\sigma^2+1/\sigma_0^2},\frac{1}{n/\sigma^2+1/\sigma_0^2})\end{aligned}\]
\[\begin{aligned} p(\sigma^2| \mu, y_1,...,y_n) &\propto p(\mu,\sigma^2 | y_1,...,y_n)\\ &\propto (\sigma^2)^{-\frac{n}{2}} exp(-\frac{1}{2\sigma^2}(y_i-\mu)^2) \cdot (\sigma^2)^{-(v_0+1)}exp[-\frac{\beta_0}{\sigma^2}]\\ &= (\sigma^2)^{-(v_0+\frac{n}{2}+1)}exp[-\frac{1}{\sigma^2}(\beta_0+\frac{\sum_{i=1}^n(y_i-\mu)^2}{2})] \\ &\propto IG(\sigma^2|v_0+\frac{n}{2},\beta_0+\frac{\sum_{i=1}^n(y_i-\mu)^2}{2})\end{aligned}\]
2.5.4 R code
The following R code first will impletement the following for \(\mu\) and \(\sigma^2\) discussed above:
\[p(\mu| \sigma^2, y_1,...,y_n) \propto N(\mu |\frac{n\bar{y}/\sigma^2+\mu_0/\sigma^2}{n/\sigma^2+1/\sigma_0^2},\frac{1}{n/\sigma^2+1/\sigma_0^2}) \] \[ p(\sigma^2| \mu, y_1,...,y_n) \propto IG(\sigma^2|v_0+\frac{n}{2},\beta_0+\frac{\sum_{i=1}^n(y_i-\mu)^2}{2})\]
update_mu<-function(n,ybar, sig2,mu_0,sig2_0)
{sig2_1=1/(n/sig2+1/sig2_0)
mu_1=sig2_1*(n*ybar/sig2+mu_0/sig2_0)
rnorm(n=1,mean=mu_1,sd=sqrt(sig2_1))}
update_sig2<-function(n,y,mu,nu_0,beta_0)
{nu_1=nu_0+n/2
sumsq=sum((y-mu)^2)
beta_1=beta_0+sumsq/2
out_gamma=rgamma(n=1,shape =nu_1,rate=beta_1)
1/out_gamma}
gibbs<-function(y,n_iter,init,prior)
{
ybar=mean(y)
n=length(y)
mu_out=numeric(n_iter)
sig2_out=numeric(n_iter)
mu_now=init$mu
## Gibbs sampler
for (i in 1:n_iter)
{
sig2_now=update_sig2(n=n,y=y,mu=mu_now,nu_0 = prior$nu_0,beta_0 = prior$beta_0)
mu_now=update_mu(n=n,ybar=ybar, sig2 = sig2_now,mu_0 = prior$mu_0,sig2_0 = prior$sig2_0)
sig2_out[i]=sig2_now
mu_out[i]=mu_now
}
cbind(mu=mu_out,sig2=sig2_out)
}
y=c(1.2,1.4,-0.5,0.3,0.9,2.3,1.0,0.1,1.3,1.9)
ybar=mean(y)
n=length(y)
prior=list()
prior$mu_0=0
prior$sig2_0=1
prior$n_0=2
prior$s2_0=1
prior$nu_0=prior$n_0/2
prior$beta_0=prior$n_0*prior$s2_0/2
hist(y,freq = FALSE,xlim=c(-3,3))
curve(dnorm(x=x,mean=prior$mu_0,sd=sqrt(prior$sig2_0)),lty=2,add=TRUE)
points(y,rep(0,n),pch=1)
points(ybar,0,pch=19)
set.seed(53)
init=list()
init$mu=0
post=gibbs(y=y,n_iter = 1e3,init=init,prior=prior)
head(post)
## mu sig2
## [1,] 0.3746992 1.5179144
## [2,] 0.4900277 0.8532821
## [3,] 0.2536817 1.4325174
## [4,] 1.1378504 1.2337821
## [5,] 1.0016641 0.8409815
## [6,] 1.1576873 0.7926196
## mu sig2
## [995,] 0.7813494 0.5510765
## [996,] 0.9622221 0.7612775
## [997,] 0.8124813 0.4640000
## [998,] 0.9434455 0.8416027
## [999,] 0.9085257 0.8193501
## [1000,] 0.9411790 0.9053170
## Warning: package 'coda' was built under R version 3.6.2
##
## Iterations = 1:1000
## Thinning interval = 1
## Number of chains = 1
## Sample size per chain = 1000
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## mu 0.9051 0.2868 0.00907 0.00907
## sig2 0.9282 0.5177 0.01637 0.01810
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## mu 0.3024 0.7244 0.9089 1.090 1.481
## sig2 0.3577 0.6084 0.8188 1.094 2.141
2.6 Convergence
2.6.1 Autocorrelation
For a sequence of random variables \(X_1,X_2,...\) that are seperated in time, we can relate it with the concept of autocorrelation. That is, the correlation between \(X_t\) with the past or future \(X_{t-l}\), which can be defined formally as follows.
\[ACF(X_t,X_{t-l})=\frac{cov(X_t,X_{t-l})}{\sqrt{Var(X_t) \cdot Var(X_{t-l})}}\]
If the sequence is stationary, such that the joint distribution of mutiple \(Xs\) does not change with time shifts, then autocorrelation for two variables does not depend on the exact times \(t\) and \(t-l\), but rather on the distance between them, namely \(l\). Thus, we can see the reason why autocorrelation plots use lags as to calculate MCMC convergence.
Autocorrelation tells us how much information is available in the Markov chain. Sampling 1000 iterations from a highly correlated Markov chain yields less information about the stationary distribution than we would obtain from 1000 samples independently drawn from the stationary distribution.
Autocorrelation is a major component in calculating the Monte Carlo effective sample size of your chain. The Monte Carlo effective sample size is how many independent samples from the stationary distribution you would have to draw to have equivalent information in your Markov chain. Essentially it is the m (sample size) we chose in the lesson on Monte Carlo estimation.
2.6.2 Step size and iteration number
Step size = variance of the proposal
Increase the variance of the proposal = Increase the step size
Increasing the variance of the proposal will decrease the acceptance rate. However, the chain will be more likely to be stable. If the chain is not stable, you can increase the iteration to reach stable. That is, you can view the chain in a longer (or, larger) iteraction scale.
For instance, m=100e3 will look more stable than m=10e3, even though they share the same sd=0.04.
library(coda)
lg=function(mu, n, ybar)
{ mu2=mu^2
n*(ybar*mu-mu2/2)-log(1+mu2)}
mu_now=10
cand_sd=5
y=c(1.2,1.4,-0.5,0.3,0.9,2.3,1.0,0.1,1.3,1.9)
ybar=mean(y)
n=length(y)
mu_storage<-c()
mu_storage<-c(mu_storage,mu_now)
acceptance_count=0
mh<-function(mu_now,m,type_MH,cand_sd)
{
for (i in 1:m)
{
if (type_MH=="randomwalk") # random walk, as mean is updated with mu_now
{
mu_cand = rnorm(n=1, mean=mu_now, sd=cand_sd)
}
else
{
if(type_MH=="independent") # independent, as mean is fixed
{mu_cand = rnorm(n=1, mean=3, sd=cand_sd)}
else
{print("woring type of MH")
break
}
}
lg_alpha=lg(mu_cand,n=n,ybar = ybar)-lg(mu_now,n=n,ybar=ybar)
alpha=exp(lg_alpha)
if(alpha>1)
{mu_now=mu_cand
acceptance_count=acceptance_count+1}
else
{random_p=runif(1)
if(alpha>random_p)
{mu_now=mu_cand
acceptance_count=acceptance_count+1}
else
{mu_now=mu_now}
}
mu_storage<-c(mu_storage,mu_now)
}
#return the following
list(mu=mu_storage,accpt=acceptance_count/m)
}
set.seed(61)
post0 = mh(mu_now=0.0, m=10e3,type_MH="randomwalk",cand_sd=0.9)
coda::traceplot(as.mcmc(post0$mu[-c(1:10)]))
## [1] 0.3829
set.seed(61)
post1 = mh(mu_now=0.0, m=10e3,type_MH="randomwalk",cand_sd=0.04)
coda::traceplot(as.mcmc(post1$mu[-c(1:10)]))
## [1] 0.9566
set.seed(61)
post2 = mh(mu_now=0.0, m=100e3,type_MH="randomwalk",cand_sd=0.04)
coda::traceplot(as.mcmc(post2$mu[-c(1:10)]))
## [1] 0.95953
## [,1]
## Lag 0 1.000000000
## Lag 1 0.575835262
## Lag 5 0.065035494
## Lag 10 -0.009494001
## Lag 50 -0.036844986
## [,1]
## Lag 0 1.0000000
## Lag 1 0.9013201
## Lag 5 0.8696353
## Lag 10 0.8327876
## Lag 50 0.5591849
## [,1]
## Lag 0 1.0000000
## Lag 1 0.9822054
## Lag 5 0.9507519
## Lag 10 0.9131846
## Lag 50 0.6545171
2.6.3 Effective sample size
## List of 2
## $ mu : num [1:100001] 10 -0.0152 -0.1007 -0.0867 -0.1421 ...
## $ accpt: num 0.96
## var1
## 414.9936
# thin out the samples until autocorrelation is essentially 0.
# This will leave you with approximately independent samples.
# The number of samples remaining is similar to the effective sample size.
coda::autocorr.plot(as.mcmc(post2$mu), lag.max=500)
thin_interval = 400 # how far apart the iterations are for autocorrelation to be essentially 0.
thin_indx = seq(from=thin_interval, to=length(post2$mu), by=thin_interval)
head(thin_indx)
## [1]<PHONE_NUMBER> 1600 2000 2400
## var1
## 250
## [1] 250
## List of 2
## $ mu : num [1:10001] 10 0 0 0.315 0.315 ...
## $ accpt: num 0.383
## var1
## 2582.095
2.6.4 Burn in
nburn = 1000 # remember to discard early iterations
post0$mu_keep = post0$mu[-c(1:1000)]
summary(as.mcmc(post0$mu_keep))
##
## Iterations = 1:9001
## Thinning interval = 1
## Number of chains = 1
## Sample size per chain = 9001
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## 0.893087 0.313314 0.003302 0.006773
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## 0.3010 0.6707 0.8873 1.1129 1.4939
## [1] 0.371181


Amanda LeMoine
Contact
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Space Invaders
Log into Space Invaders to Play
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Update the database to eat burgers
Wedding Scheduler Around Golden Hour
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Weather App
Weather Forecast with Search History


What? No, there isn't anyone here ... (he laughs in amusement), perhaps you saw the Ghost of the local building. What ... you never heard of the stories? Really? It was all the rage a while back, well let me tell you the story. So about ten years back there was a man named Ted Rogers who worked here, Ted was much like you ... except he had a family, not that uhhh ... there's anything wrong with you. Anyways, Ted busted his balls for his family often spending late nights working all so that he can see his wife and daughter on the Weekends. Now I knew Ted personally, and he was a cheerful guy ... really terrific guy, always said 'hi' to you and always made time to help others. So I knew something was wrong when he came in one day and didn't talk at all ... he just had a dejected look in his eyes, and didn't answer when anyone asked him what was wrong. Later that day the Police showed up and asked for him it turned out he had murdered his wife after he found her in bed with another man, he blew her brains out with a shot gun. When we came to check on him in his office, we found him hanging from the ceiling fan ... after that some ... strange occurence have happened in the Office. I personally don't believe it though.(For a moment you can swear that you saw his eyes turn pitch-black, though as soon as you blinked it turned back to normal)
1). Hmmh ... thanks for telling me, I gotta go now though ...


This document lists the currently known problems with the pywin.debugger package.
You may also wish to view the debugger overview, the debugger tutorial, or the general debugger documentation.
There is a problem with the "hierlist" control on Windows 95. I don't know what it is.
This is particularly true when debugging non-Pythonwin applications. I have made some efforts in this area, but it is pretty hard and complex.
Closing the debugger dialog, selecting "End/Close" from one of the property pages, or "Close" from the debugging toolbar should always work as expected.
If you are ever debugging a function that is the only function in the call-stack (ie you are at the bottom stack), then the functions "run" "step out" and "step over" may not work as expected - each of these operations may function as a single-step.
You will need to single-step out of this entire function.
This is a restriction in the core Python interpreter, caused by the introduction of keyword arguments. When Python supports it again, so will I.


Xmake is a lightweight cross-platform build utility based on Lua.
It is very lightweight and has no dependencies because it has a built-in Lua runtime.
It uses xmake.lua to maintain project builds and its configuration syntax is very simple and readable.
We can use it to build project directly like Make/Ninja, or generate project files like CMake/Meson, and it also has a built-in package management system to help users solve the integrated use of C/C++ dependent libraries.
Xmake = Build backend + Project Generator + Package Manager
Introduction of new features
More flexible package extensions
Now, we can inherit all the configuration of an existing package through the set_base
interface, and then rewrite part of the configuration on this basis.
This is usually in the user’s own project, it is more useful to modify the built-in package of the official repository of xmake-repo, such as: repairing and changing urls, modifying the version list, Install logic and more.
For example, modify the url of the built-in zlib package to switch to your own zlib source address.
package("myzlib")
set_base("zlib")
set_urls("https://github.com/madler/zlib.git")
package_end()
add_requires("myzlib", {system = false, alias = "zlib"})
target("test")
set_kind("binary")
add_files("src/*.c")
add_packages("zlib")
We can also use it to simply add an alias package.
package("onetbb")
set_base("tbb")
We can install the tbb package through add_requires("onetbb")
integration, but the package name is different.
Package management supports toolchain switching
Previously, we limited the toolchains that can only be installed under the cross platform to switch packages. In the new version, we can support the switchover of toolchains under more platforms.
E.g:
$ xrepo install --toolchains=clang zlib
We can quickly switch to the clang toolchain to compile and install the zlib library on platforms such as linux.
We can also switch them in the xmake.lua configuration file.
add_requires("zlib", {configs = {toolchains = "gcc-11"}})
The zlib packages installed by different tool chains will be stored in different directories without interfering with each other, and there will be no link compatibility problems caused by compiler differences.
Built-in package virtual environment
The Xrepo command has already well supported package virtual environment management, xrepo env shell
, but for complex package environments, users still need to configure an xmake.lua file to manage their own package environment.
For example, we need a common development environment shell with common development toolchains such as cmake, python and vs/autoconf by default, and we need to create a configuration file devel.lua by ourselves.
add_requires("cmake")
add_requires("python")
if is_host("linux", "bsd", "macosx") then
add_requires("pkg-config", "autoconf", "automake", "libtool")
elseif is_host("windows") then
set_toolchains("msvc")
end
Then, execute the following command to import into the global configuration.
$ xrepo env --add devel.lua
In this way, we can load the shell and bind this environment with the following command:
$ xrepo env -b devel shell
> cmake --version
cmake version 3.19.3
In the new version, we have built in some commonly used environments, which can be viewed through xrepo env -l
:
$ xrepo env -l
- msvc
- llvm-mingw
- llvm
- mingw-w64
-devel
- python3
- depot_tools
- python2
Among them, devel is also in it, so we only need to execute xrepo env -b devel shell
to bring up a devel development environment without configuring them yourself.
Such as python, msvc, etc. are also some of the more commonly used environments, which can be used directly.
Of course, we also support temporarily creating an xmake.lua locally to configure the loading package environment instead of placing it in the global configuration.
Custom installation package download
We can customize the download logic of the package through the new on_download
interface, which is usually not used, and it is enough to use Xmake’s built-in download.
If the user builds a private repository and has a more complex authentication mechanism and special processing logic for the download of the package, the internal download logic can be rewritten to achieve this.
on_download(function (package, opt)
-- download packages:urls() to opt.sourcedir
end)
In the opt parameter, you can get the destination source directory opt.sourcedir
of the downloaded package. We only need to get the package address from package:urls()
and download it.
Then, add some custom processing logic as needed. In addition, you can add download cache processing and so on.
ASN.1 Program Build Support
ASN.1 programs need to use ASN.1 Compiler to generate relevant .c files to participate in project compilation.
While Xmake provides built-in add_rules("asn1c")
rules to process .c
file generation, add_requires("asn1c")
automatically pulls and integrates ASN.1 compiler tools.
Here is a basic configuration example:
add_rules("mode.debug", "mode.release")
add_requires("asn1c")
target("test")
set_kind("binary")
add_files("src/*.c")
add_files("src/*.asn1")
add_rules("asn1c")
add_packages("asn1c")
For details, see Complete Example Project.
Support for building Swift programs on all platforms
Previously, Xmake only supported the construction of Swift programs under macOS with the help of the Xcode toolchain. In the new version, we have also made improvements to allow the swift toolchain to be used independently, and to support the construction of swift programs on linux/windows. The usage is the same as before.
Supports export of specified symbol list
In previous versions, we provided utils.symbols.export_all
for automatic full symbol export of dll libraries for windows.
Although this is very convenient, it can only support windows programs, and the full export does not control the size of the generated dll, and there may be many internal symbols that are not needed at all to be exported.
However, the utils.symbols.export_list
rule provided by our new version can directly define the list of exported symbols in xmake.lua, for example:
target("foo")
set_kind("shared")
add_files("src/foo.c")
add_rules("utils.symbols.export_list", {symbols = {
"add",
"sub"}})
Alternatively, add a list of exported symbols in the *.export.txt
file.
target("foo2")
set_kind("shared")
add_files("src/foo.c")
add_files("src/foo.export.txt")
add_rules("utils.symbols.export_list")
For a complete project example, see: Export Symbol Example
By specifying symbol export, we can make the generated dynamic library as small as possible, and do not export irrelevant internal symbols at all. In addition, this rule supports linux, macOS and windows, which is more general.
Internally it automatically uses .def, version scripts and --exported_symbols_list
to handle symbol exports.
Built-in support for linker scripts
In the new version, we also have built-in support for linker scripts and version scripts files, we can use add_files
to add them directly without configuring add_ldflags("-Txxx.lds")
.
Currently .ld
and .lds
are supported as linker scripts configuration files to add:
add_rules("mode.debug", "mode.release")
target("test")
add_deps("foo")
set_kind("binary")
add_files("src/main.c")
add_files("src/main.lds")
We also support .ver
, .map
suffix files to be added as version script.
target("foo")
set_kind("shared")
add_files("src/foo.c")
add_files("src/foo.map")
The content of the foo.map file is as follows:
{
global:
foo;
local:
*;
};
Changelog
New features
- #2011: Support to inherit base package
- Support to build and run xmake on sparc, alpha, powerpc, s390x and sh4
- Add on_download for package()
- #2021: Support Swift for linux and windows
- #2024: Add asn1c support
- #2031: Support linker scripts and version scripts for add_files
- #2033: Catch ctrl-c to get current backtrace for debugging stuck
- #2059: Add
xmake update --integrate
to integrate for shell - #2070: Add built-in xrepo environments
- #2117: Support to pass toolchains to package for other platforms
- #2121: Support to export the given symbols list
Changes
- #2036: Improve xrepo to install packages from configuration file, e.g.
xrepo install xxx.lua
- #2039: Improve filter directory for vs generator
- #2025: Support phony and headeronly target for vs generator
- Improve to find vstudio and codesign speed
- #2077: Improve vs project generator to support cuda


Projects
Writing
Resumé
Thoughts, ideas, and ramblings
The Art Institute of Chicago
04 Mar 2016
Free admission for Illinois residents on Thursday evenings.
Calluna
24 Feb 2016
On the typography of wilburchen.com
We’re back!
23 Feb 2016
and here to stay for a while.
Literally the worst.
19 Feb 2016
a rant—with screenshots!
NewYearNewMe
01 Feb 2016
…because January doesn’t count—that was a test run.


DataSourceAttribute is used on individual parameters of a test method to identify the source from which arguments will be suppoied. The attribute has four public constructors.
DataSourceAttribute(Type sourceType, string name1, params string[] moreNames); DataSourceAttribute(string name1, params string[] moreNames); // For CLS-Compliance FactoriesAttribute(Type sourceType, string sourceName); FactoriesAttribute(string sourceName);
If sourceType is specified, it represents the class that provides the data. If not, the class containing the test method is used.
Each sourceName, represents the name of a field, property or method that will provide the arguments.
Each individually named source has the following characteristics:
NUnit locates the data sources at the time the tests are loaded, creates instances of each class having non-static sources and builds a list of tests to be executed. Each instancet is only created once at this time and is destroyed after all tests are loaded. In the case of sources located within the same class as the tests, a different instance is created each time the test is run, so no communication is possible using non-static members of the class.


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Kartik Aaryan Gets Trolled For Sporting Fake Abs In A Commercial; Check Out The Users’ Reactions
Kartik Aaryan surprised everyone with Sonu Ke Titu Ki Sweety through a transformation of being a chocolate boy to hot hunk with chiselled body.
by Koimoi.com TeamKartik Aaryan has been through a huge makeover ever since he became a household name as a Punchnama boy. He surprised everyone with Sonu Ke Titu Ki Sweety through a transformation of being a chocolate boy to hot hunk with chiselled body. Following his current box office credibility, the actor is favourite amongst the top producers and advertising brands.
Recently, in one of his commercials, the 29-year-old showcased his abs but got mercilessly got trolled by getting tagged as ‘photo shopped’. Social media users trolled him for his fake abs, with one of them calling him the recipient of Best Body VFX award.
The commercial is about the hair removal cream of men.
Below are the users’ reactions:
Meanwhile, as a part of promotion of Pati Patni Aur Woh, Kartik Aaryan graced Zoom’s chat show, where he opened up like never before.
On Zoom’s “By Invite Only” chat show, Kartik opened up about his choices in life and talked about what is easier for him to quit – acting or sex
He said: “Acting and sex are like bread and butter. You can’t quit either. Acting and sex or love goes hand in hand for me.”
Kartik added: “I don’t like talking about my personal life but I don’t like hiding it also. I won’t stop going for a dinner at a restaurant with someone just because there will be paparazzi there.”


Women's Clothing
When the temperatures plummet, we look for clothing and clothing accessories to protect us from cold and wind chill.
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Camping is awesome
Check out our amazing words which we write about camping and stuff.


Cottonwood is a new and upcoming private island resort, located on the coast of Malindi, just a few hundred meters south of Watamu.
Cottonwood offers a wide range of activities, from deep sea diving to snorkelling, it would be hard for anybody to claim boredom! Click here to learn more.
Travel arrangements can be made via direct reservation, or through TripAdvisor, which will give you the best prices for our services. Click here to learn more.
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About The Site


--- title: "Start" date: 2018-12-16T18:21:16+01:00 draft: false headerimage: logo.png ---
After a lot of planning, we are happy to announce that we'll be hosting all you lovely Django people next year!
You don't necessarily need to buy plane tickets or book hotel rooms yet: Alternatives such as ride sharing and accommodation sharing will be provided through the website for interested ticket-holders from December and onward.
Here's our plan:
October:
Early organizing of the conference (volunteers, get in touch!) ✔
November: More organizing, website is booted with a CfP ✔
December: Ticket sale opens, including info on scholarships
January: Early bird/Sharing Pioneer discount expires
January 13th: CfP closes
March: Talks start being published
April: Main conference (10-12th), sprints (13-14th) and opportunities to do workshops before and after


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This story is part of a series of specially designed stories that represents some of the best journalism The Post has to offer. Check out the rest of the special projects here.
Anastasia Nicholas / For The Post
With his vibrant demeanor and a tie-dye lab coat to match, it’s difficult to miss Frazier Nyasulu in Clippinger Laboratories.
Voted most frequently seen in the laboratory by Post readers, Associate Professor Nyasulu teaches entry-level chemistry and biochemistry labs. This fall, he instructs nearly 1700 students, he said.
“All these students will see my pretty face at least once or twice a week,” Nyasulu said.
Nyasulu said his signature lab coat alerts students to his presence.
“My lab coat does a few things — tells students the monster is here,” he laughs. “I think that’s why students notice me. ‘Frazier is in the house!’ ”
The lab coat is a topic of frequent discussion among students, who often ask where it is from, Nyasulu said. It was a gift from the stockroom worker at the laboratory.
“I’d like to get credit for it some other way, but that’s the way I got it,” Nyasulu said. “I probably said, ‘I like that.’ If I say I like it, that’s my way of saying, ‘Can I have it?’ ”
Courtnee Ambos, a junior studying biology, said she often sees — or hears — Nyasulu around the lab.
“Every time, we can hear his laugh from down the hallway,” Ambos said. “I’d say I see him about three times a week.”
Haylie Kinman, a freshman studying biological science, said she loves his lab coat. She agreed that he was the most likely to be seen in the lab, and she said she sees him once a week because he would come into every lab.
“He’s very friendly, very nice, very easy to talk to, and he’s just got a fun personality,” Kinman said.
Nyasulu received his doctorate in analytical chemistry at University of Salford in Manchester, England.
During the about 11 years he has worked at OU, Nyasulu said there have been no accidents in his lab, other than minor ones, such as a student touching something hot.
“I love my job, I love what I’m doing and I love my students. I have the best job ever.”– Frazier Nyasulu
He said his technique for preventing accidents is to design labs so that students are dealing with materials that won’t harm them.
“Students care about their safety,” Nyasulu said. “We take care of them, and they take care of themselves.”
Katlyn Meeks, a sophomore studying biological studies, said Nyasulu is very knowledgeable and safety-oriented. She sees him in the lab at least once a week.
“He’s a very nice person, so he’s very approachable,” she said.
Nyasulu said his students always remember him, but he can’t pinpoint the reason why.
“Maybe because of my laugh,” he said. “I love my job, I love what I’m doing and I love my students. I have the best job ever.”
– Kevin Pan contributed to this report
Photo by: Hannah Ruhoff / Staff Photographer
Development by: Taylor Johnston / Digital Production Editor
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