P533 Bayesian data analysis, Prof. John K. Kruschke

Spring 2016: Tu,Th 11:15am-12:30pm, Room 109 Psych.

Overview: P533 is a tutorial introduction to doing Bayesian data analysis. The course is intended to make advanced Bayesian methods genuinely accessible to real graduate students. Advanced undergrads are also welcome. The course covers all the fundamental concepts of Bayesian methods, and works from the simplest models up through hierarchical models applied to various types of data. More details about content are provided below in the daily Schedule of Topics. Students from all fields are welcome and encouraged to enroll (see figure at right). The course uses examples from a variety of disciplines.

Prerequisites: This is not a mathematical statistics course, but some math is unavoidable. If you can understand basic summation notation like Σi xi and integral notation like ∫ x dx, then you're in good shape. We will be doing a lot of computer programming in a language called R. R is free and can be installed on any computer. The textbook includes an introductory chapter on R. A previous course in traditional statistics or probability can be helpful as background, but is not essential. P533 proceeds independently of traditional ("null hypothesis significance testing") statistical methods.

Credit toward I.U. Statistics Department requirements: P533 counts toward the Ph.D. minor in STAT and toward the 12 hour "area relevant to statistics" section of the MSAS (Masters in Applied Statistics).

Grading: There are homework exercises assigned every week. No exams or projects. All assignments are mandatory. There will be penalties for late homework unless you have a cogent excuse. These penalties are designed as an incentive to you because the material is cumulative; the penalties also help keep things fair to all students. If you must be late with an assignment, please notify Professor Kruschke immediately. Grades will be determined by total points on the homework assignments, as a percentile relative to other students in the class. There is no pre-set threshold for letter grades, nor any pre-set quota for the number of A’s, etc. As this is a graduate-level course, grades are usually high, but occasionally low grades are assigned when appropriate.

Required textbook: Doing Bayesian Data Analysis, 2nd Edition: A Tutorial with R, JAGS, and Stan. Go to the web page, https://sites.google.com/site/doingbayesiandataanalysis/purchase, for a link to purchase the book with a 30% publisher’s discount. (The course uses the 2nd edition, which has a lot of material that is not in the 1st edition.)

Instructor: John K. Kruschke, johnkruschke@gmail.com. Office hours by appointment; please do ask.

Assistant: TBD.

Discussion: Please discuss the assignments and lectures on Canvas. If you are attending the class but cannot get access to the Canvas page, please email Prof. Kruschke.

Disclaimer: All information in this document is subject to change. Changes will be announced in class.

 Schedule of Topics Exact day of each topic might flex as course progresses, in response to student progress and interests. Week Day Chapter and topic 1 Tu 2. Introduction: Credibility, models, and parameters. 1 Th 3. The R programming language. Instructions for installation of software are here: https://sites.google.com/site/doingbayesiandataanalysis/software-installation 2 Tu 4. Probability. 2 Th 5. Bayes’ rule. 3 Tu 6. Inferring a probability via mathematical analysis. 3 Th 7. Markov chain Monte Carlo (MCMC). 4 Tu 8. JAGS. 4 Th 8, continued. 5 Tu 9. Hierarchical models. 5 Th 9, continued. 10. Model comparison. 6 Tu 10, continued. 11. Null hypothesis significance testing (NHST). 6 Th 11. NHST, continued. 7 Tu 12. Bayesian null assessment. See also article titled “Bayesian assessment of null values via parameter estimation and model comparison” at http://www.indiana.edu/~kruschke/articles/Kruschke2011PoPScorrected.pdf 7 Th 12, continued. See also manuscript titled “The Bayesian new statistics: Two historical trends converge” at http://ssrn.com/abstract=2606016 8 Tu 13. Goals, power, and sample size. See also video at http://www.youtube.com/playlist?list=PL_mlm7M63Y7j641Y7QJG3TfSxeZMGOsQ4. 8 Th 13, continued. 9 Tu 15. The generalized linear model. 16. Metric predicted variable, 1 or 2 group predictor variable. 9 Th 16, continued. Also power analysis. See article titled “Bayesian estimation supersedes the t test” at http://www.indiana.edu/~kruschke/BEST/. 10 Tu 17. Metric predicted variable, metric predictor variable. 10 Th 17, continued. 18. Metric predicted variable, metric predictor variables. See also article titled “The time has come: Bayesian methods for data analysis in the organizational sciences” at http://www.indiana.edu/~kruschke/BMLR/. 11 Tu 18, continued. 11 Th 19. Metric predicted variable, nominal predictor variable. 12 Tu 19, continued. 20. Metric predicted variable, nominal predictor variables. 12 Th 20, continued. 13 Tu 21. Dichotomous predicted variable (logistic regression). 13 Th 22. Nominal predicted variable (softmax regression). For an applied example of hierarchical conditional logistic regression, see article titled “Ostracism and fines in a public goods game with accidental contributions: The importance of punishment type” at http://journal.sjdm.org/14/14721a/jdm14721a.pdf 14 Tu 22, continued. 14 Th 23. Ordinal predicted variable (ordinal probit regression). For another example, see manuscript titled “Moral Foundation Sensitivity and Perceived Humor” at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2519218 15 Tu 23, continued. 15 Th 24. Count predicted variable. Finals No final exam, but final homework is due during finals’ week at date TBA.