PSYC272 Seminar in Bayesian Statistics
http://psy2.ucsd.edu/~dhuber/bayesian_stats.html
Class: Wed 1012:50 Room: MNDLR 1507
Professor: David Huber Office Hours: Tu 1012 Office: 5137 McGill
Course Description:
Bayesian Statistics offer an alternative to traditional null hypothesis testing. Traditional significance tests of ANOVA, correlation, and ttests calculate the probability of observing the results you found (or results more extreme than what you found), under the assumption that there were no effects. But what you really want is the opposite. You don't want to know the probability of your data under the null; instead you want to know the probability that there was or was not an effect. This can be done with Bayesian statistics and, furthermore, it can be done in the framework of our favorite traditional analyses (correlations, ttests, ANOVAs, etc.). In this course we will cover the mathematics behind Bayesian statistics and we will use the R programming language to learn how to do Bayesian analyses.
Readings:
Kruschke, J. K. (2011). Doing bayesian data analysis : a tutorial with R and BUGS. Burlington, MA: Academic Press.
http://www.indiana.edu/~kruschke/DoingBayesianDataAnalysis/
Available on Amazon for $74.68. 2 days shipping is $11.98, but free with Amazon Prime.
Requirements:
R programming
The textbook is full of source code for implementing Bayesian statistics using R (and WinBUGS). As you read through the chapters each week, keep R open on your computer, and try out some of the analyses. Once you’re done reading the chapters for that week, save the history of your commands, and email the results to me in advance of class. There is no right or wrong way to do this and no guidelines on how much or how little you need to do. I’m just looking for evidence that you tried it out.
Student Led Discussions
Each student will lead the class in covering one chapter. This will take place during one of the two hours of class. You do not need to fully understand the chapter. However, it is your responsibility to either show us with power point, or write on the board the main issues, equations, figures, etc. covered in the chapter. Your goal is to make sure that we fully cover the material and that collectively we figure it out.
Class Discussion
You are required to read two chapters
every week. In class participation is expected, with everyone providing
comments during every class.
Class
Links
· Downloadable R code from the book
· R studio
· Cory’s notes on MCMC using a Mac with JAGS
o HotHand
o SimpleLinearRegressionRepeatedBrugs
o MultipleLinearRegressionBrugs
o LogisticOnewayAnovaHeteroVarBrugs
o MultipleLogisitcRegressionBrugs
· Rather than modifying your code to make it compatible with JAGS, you could try FakeBRugs
· A new article investigating the many ways to cheat with NHST
Schedule of Class Meetings
Date 
Chapters 
Discussion Led By 
Sep. 28

Introduction 

Oct. 5

Chapters 1+2 (no one leads) Chapter 3: Probability Chapter 4: Bayes’ Rule 
Carson Mallorie 
Oct. 12

Chapters 5+6: Binomial Distribution Chapter 7: Metropolis Algorithm 
Esther Jordan 
Oct. 19

Chapter 8: Gibbs Sampling Chapter 9: Hierarchical Prior 
Rachel Jon 
Oct. 26

Chapters 10: Model Comparison Chapter 11: NHST 
Liz Nicole 
Nov. 2

Chapter 12: Bayesian Null Hypothesis Chapter 13: Bayesian Power Analysis 
Evan Andy 
Nov. 9

Chapter 14: the GLM Chapter 15: Bayesian One Sample ttest 
Megan Bernhard 
Nov. 16

Chapter 16: Bayesian Regression Chapter 17: Bayesian Multiple Regression 
Cara Dave 
Monday
Nov. 21 (57pm)

Chapter 18: Bayesian Oneway ANOVA Chapter 19: Bayesian Multifactor ANOVA 
Erik
Sirawaj (Sean) 
Nov. 30

Chapter 20: Bayesian Logistic Regression Chapter 22: Bayesian ChiSquare 
Kevin Cory 