P533
Bayesian data analysis, Prof. John K. Kruschke
Spring 2015: Tu,Th 9:3010:45am, 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 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 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:
As of Spring 2015, 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 Wednesdays 1:302:30, PY 243.
Discussion: Please discuss the assignments and
lectures on Oncourse
under the "Forums" link. If you are attending the class but cannot
get access to the Oncourse
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/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 at http://www.indiana.edu/~kruschke/articles/Kruschke2011PoPScorrected.pdf 
7 
Th 
12, continued 
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 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 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 another example,
see article 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 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. 