

P533/P534 is a tutorial introduction to doing Bayesian statistics for data analysis. The course is intended to make advanced Bayesian methods genuinely accessible to real graduate students, and even unreal undergraduates (see prereq's below). Many complete computer programs are provided for you do adapt to your own research.
• In P533, we start from the basics of probabilities and Bayes' theorem, and gradually work our way through contemporary Monte Carlo methods in the context of simple analyses, building up to simple linear regression and Bayesian versions of singlefactor analysis of variance (ANOVA).
• In P534, we contrast null hypothesis significance testing with Bayesian approaches to null value assessment, as well as Bayesian approaches to power. Then we do a variety of more complicated realistic applications, covering the Bayesian versions of multiple linear regression, logistic regression, analysis of variance, etc., including a look at repeatedmeasures designs.
• The order of topics (as just described) is different from previous years, so that students who can only enroll in P533 (without continuing into P534) nevertheless learn about basic applications such as simple linear regression and singlefactor ANOVA.
More details about the schedule are provided in tabular form below, in the context of chapters from the course textbook.
Why go Bayesian? See Figure 1, above. But beyond that, sciences from astronomy to zoology are changing from 20thcentury nullhypothesis significance testing to Bayesian data analysis, because Bayesian analysis provides rich information with flexible application to numerous models. Read more: ^{*}Your click on this link constitutes your request to the author for a personal copy of the article exclusively for individual research, and the author's delivery of that copy. Any other use is prohibited.
 An article that shows the rich information provided by Bayesian estimation in the context of analyzing data from two groups: Kruschke, J. K. (2012). Bayesian estimation supersedes the t test. Journal of Experimental Psychology: General ^{*}. More info, including links to videos, is here.
 An overview article: Kruschke, J. K. (2010). Bayesian data analysis. Wiley Interdisciplinary Reviews: Cognitive Science ^{*}, 1(5), 658676.
 An article that emphasizes that Bayesian data analysis is appropriate regardless of the status of Bayesian models of cognition: Kruschke, J. K. (2010). What to believe: Bayesian methods for data analysis. Trends in Cognitive Sciences ^{*}, 14(7), 293300.
 An article that explains two Bayesian methods to assess null values, and which one is typically more informative: Kruschke, J. K. (2011). Bayesian assessment of null values via parameter estimation and model comparison. Perspectives on Psychological Science ^{*}, 6(3), 299312.
Prerequisites:
Textbook: Doing Bayesian Data Analysis: A Tutorial with R and BUGS, by J. K. Kruschke. Academic Press, 2011.
Schedule  
Week of semester  Course  Book Chapters 

1  P533  Ch. 1, 2, 3: Intro; probability; R 
2  P533  Ch. 4: Bayes' rule 
3  P533  Ch. 5, 6: Beta distribution 
4  P533  Ch. 7, 8: Markov chain Monte Carlo (excluding 7.3.3 and 7.4.3) 
5  P533  Ch. 9: Hierarchical models 
6  P533  Ch. 14: Generalized linear model Ch. 15: Normal distribution. 
7  P533  Ch. 16: Linear regression 
8  P533  Ch. 18: Oneway ANOVA Ch. 23: How to report an analysis 
9  P534  Ch. 10: Model comparison (including 7.3.3 and 7.4.3) 
10  P534  Ch. 11: Null hypothesis significance testing Ch. 12: Bayesian approaches to null value assessment 
11  P534  Ch. 13: Power 
12  P534  Ch. 17: Multiple linear regression 
13  P534  Ch. 20: Logistic regression Ch. 21: Ordinal regression 
14  P534  Ch. 19: Two factor ANOVA 
15  P534  Ch. 22: Contingency table analysis 
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.
Grading; Homework; Exams: There are homework exercises assigned every week or so. No exams or projects. Grades will be determined by performance on the homework assignments. 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 the professor immediately.
How does this course (P533/P534) differ from S626? The Dept. of Statistics offers S626, Bayesian theory and data analysis. S626 has a prerequisite of "two statistics courses at the graduate level", and provides a mathematical treatment of Bayesian data analysis. Students are encouraged to consider S626 after taking P533/P534.
Disclaimer: All the information here is subject to
change. Changes will announced in class.
This web page is at URL = http://www.indiana.edu/~jkkteach/P533/