P533
Bayesian Data Analysis, Prof. John K. Kruschke
Spring 2017: Tu,Th 9:30am10:45am, 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 graduate students in the
social sciences. 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 (a.k.a. multilevel models) applied to
various types of data. More details about content are provided below in the 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 understand basic
summation notation like Σ_{i} x_{i} 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 preset threshold for
letter grades, nor any preset quota for the number of A’s, etc. As this is a
graduatelevel 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: Torrin
Liddell, tliddell@indiana.edu. Office hours to be posted on Canvas.
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. 

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/softwareinstallation 
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
“The Bayesian New Statistics” at https://osf.io/dktc5/ 
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. 