Ryan Bakker
Assistant Professor
Department of Political Science
University of Georgia
416 Baldwin Hall
Athens, GA 30602
706.542.0813
rbakker [AT] uga [DOT] edu

Introduction to Applied Bayesian Analysis
(UGA Maymester)

This course introduces the basic theoretical and applied principles of Bayesian statistical analysis in a manner geared toward students in the social sciences. The Bayesian paradigm is particularly useful for the type of data that social scientists encounter given its recognition of the mobility of population parameters, its ability to incorporate information from prior research, and its ability to update estimates as new data are observed. Download Course Syllabus

FSU Workshop

Lecture Slides:

Lecture 1
Lecture 2
Probability Review
Lecture 3
Lecture 4
Lecture 5
Lecture 6 (Intro to Winbugs)
Lecture 6 (Convergence diagnositics)
Lecture 7
Lecture 8
Lecture 10

Homework:

HW1
data for HW1
HW2
HW3
data for HW3
HW4
data for HW4
HW5
data for ologit data for mnl
HW6
data for HW6

Missing Data:

missing.model1
missing.model2
missing.model3
missing.data1
missing.data.2_3

Multi-Level Models:

wvs.code
wvs.data
READ.ME file for code below
vary.int1.code
vary.int1.data
vary.int.cov.code
vary.int.cov.data
var.int.time.code
var.int.time.data
vary.slope.code
vary.slope.int.code
var.slope.data
corporatism
MLM ologit
immigration example
immigration example (stata)

Random-walk prior stuff

UK-CMP data 

Model

BAM code

Sample Code:

R code for sampling from beta-binomial posterior
simulating integrals
predictive.distributions
sampling.stuff
pumps example: jags_
undervote
linear.model.example
angell.data
linear.add.ons
logit.code
logit.data
logit.predprob
ordered.logit
mnl.example
logit.forecast.code
logit.forecast.data
beer.data
factor.model.code
factor.model.data
IRT model
data with structure code
writeDatafileR

mnl_data

:

Why Everyone Isn't a Bayesian
Bayesian Inference for Comparative Research