P533
Bayesian Data Analysis, Prof. John K. Kruschke
Spring 2017: Tu,Th 9:30am10:45am,
Room 115 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:
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: <name, email, office hrs and
place 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/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 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. 