Hard cover print book of DBDA2E back in stock, with 30% discount

Here for link to publisher with 30% discount.

The hardcover print book of DBDA2E is finally back in stock at the publisher. Go here for a link to the publisher that has a 30% discount.

Exercise Solutions for Chapters 1-14 of DBDA2E are now available

Exercise Solutions for Chapters 1-14 of DBDA2E are now available at the book's web site.

June & July workshops in Bayesian data analysis

Full list of workshops is here. Upcoming workshops include these:

2015 June 1 - 5. Five-day course: Doing Bayesian Data Analysis, at Stats Camp, Dallas, Texas.

2015 June 15-19. Five-day course: Doing Bayesian Data Analysis. University of St. Gallen, Switzerland.

2015 June 22-24. Three-day workshop: Doing Bayesian Data Analysis. University of Edinburgh, Scotland.

2015 July 7 - 10 (tentative dates). Four-day course: Doing Bayesian Data Analysis. Interuniversity Consortium for Political and Social Research, Ann Arbor, Michigan.

An example of hierarchical conditional-logistic Bayesian estimation, applied to punishment choice in a public goods game.

Conditional-logistic regression and softmax regression (a.k.a. multinomial logit regression) are covered in Chapter 22 of DBDA2E, but no examples of hierarchical versions are given in that chapter. An example of hierarchical conditional-logistic Bayesian estimation, applied to punishment choice in a public goods game, is provided in a new article. The appendix of the article (linked below) provides complete details of the model.

Abstract: Punishment is an important method for discouraging uncooperative behavior. We use a novel design for a public goods game in which players have explicit intended contributions with accidentally changed actual contributions, and in which players can apply costly fines or ostracism. Moreover, all players except the subject are automated, whereby we control the intended contributions, actual contributions, costly fines, and ostracisms experienced by the subject. We assess subject’s utilization of other players’ intended and actual contributions when making decisions to fine or ostracize. Hierarchical Bayesian logistic regression provides robust estimates. We find that subjects emphasize actual contribution more than intended contribution when deciding to fine, but emphasize intended contribution more than actual contribution when deciding to ostracize. We also find that the efficacy of past punishment, in terms of changing the contributions of the punished player, influences the type of punishment selected. Finally, we find that the punishment norms of the automated players affect the punishments performed by the subject. These novel paradigms and analyses indicate that punishment is flexible and adaptive, contrary to some evolutionary theories that predict inflexible punishments that emphasize outcomes. Keywords: punishment, public goods game, ostracism, trembling-hand, intention, outcome bias.

Liddell, T. M., and Kruschke, J. K. (2014). Ostracism and fines in a public goods game with accidental contributions: The importance of punishment type. Judgment and Decision Making, 9(6), 523-547. The article is here (http://journal.sjdm.org/14/14721a/jdm14721a.pdf).

How can I learn Bayesian modeling?

An email inquires:

Dear Sir, Greetings. I am a PhD ... student based in [country]. I work in the area of [...] and much of the new models in this area are based on Bayesian approach. I too feel this approach needs to [be] adopted ... . I Sir would too like to learn this approach. Unfortunately here in [country] we in Social Science are [not] trained too well in methodology. This mail is to request you to guide me as to how I can learning Bayesian Modeling relevant to [...] by self-study. There are currently no courses in [country] (to the best of my knowledge) that offer Bayesian Statistics/Bayesian Modeling. Please suggest resources (books, articles, videos) and methodology. I sincerely thank you for going through my mail and taking out you valuable time to help me. Thanks and Regards,

My reply:

Dear [...]:

Thank you for your interest.

Well, there's a vast literature out there, but since you're asking me, I'll recommend the following sequence from my own work.

For an introduction to Bayesian data analysis:
1. Read Chapter 2 of the 2nd Edition of my book. You can preview the book at various sites listed here.
2. Read the article, "Bayesian estimation supersedes the t test," linked here.
3. Read the article, "The time has come: Bayesian methods for data analysis in the organizational sciences," linked here.

For an introduction to Bayesian models of perception and cognition:
1. Read the blog post, "Bayesian models of mind, psychometric models, and data analytic models," linked here.
2. Read the article, "Bayesian learning theory applied to human cognition," linked here.
3. Read the article, "Bayesian approaches to associative learning: From passive to active learning," linked here.

Good luck with your work, and thanks again for your interest.

John

Warning message in runjags turned off in new version of DBDA2E utilities

A reader pointed out that programs that accompany DBDA2E and use runjags produce a disconcerting warning message about "starting parallel chains without setting different PRNG for each chain. Different .RNG.name values have been added to each set of initial values." This is merely runjags saying that it is doing the right thing, and not to worry. But it seems to cause worry. Therefore, in a new version of the DBDA2E utilities program I have set the options in runjags not to produce the warning message. To get the new version, go to the software web page and scroll down to the bottom where you will find the programs for the book. Click the download arrow on the right-hand side. Be sure to unzip/extract the file.

eBook and Kindle of DBDA2E now available

The eBook (PDF) and Kindle editions of Doing Bayesian Data Analysis, 2nd Edition, are now available. The book web page has relevant links.