Robert B. Gramacy Professor of Statistics
Bayesian Inference
BUS 41913 is a graduate course in Bayesian Inference.
The course will focus on understanding the principles underlying
Bayesian modeling and on building experience in the use of Bayesian analysis for making
inference about real world problems. Particular attention will be paid to the computational
techniques (e.g., MCMC) needed for most problems and their implementation
in the R
language for
statistical computing
Lectures & Demos

Parts 0 & 1: Introduction and fundamentals
Demos: Rare event and binomal, Poisson, and normal examples 
Part 2: One parameter models
Demos: beta binomal, and poisson examples on education v. fertility (datafile) and heart transplants 
Part 3: Monte Carlo inference
Demos: plugin Monte Carlo, rejection sampling, and importance sampling 
Part 4: Multiparameter and normal models
Demos: midge, IQ, and CBS poll 
Part 5: MCMC: Metropolis and Gibbs samplers
Supplementary MH proof sketch
Demos: Gibbs (for normals), MetropolisHastings (for normals), a RWMH example, and effective sample size 
Part 6: Multivariate normal and linear models
Demos: bivariate normals, random Wishart draws, reading (datafile), and O2 (datafile) 
Part 7: Hierarchical modeling
Demos: math scores (datafile), and rat tumors (datafile) 
Part 8: Model criticism, selection, and averaging
Demos: diabetes, and RJ MCMC 
Part 9: GLMs and hierarchical LMs and GLMs
Demos: binomial links, sparrows (datafile), math scores (2) (datafile), and mice (datafile) 
Part 10: Latent variables and missing data
Demos: data augmentation
Homework Due at the start of lecture
 Homework 1 for Parts 01, due 6 April 2016
Solutions: pdf  Homework 2 for Part 2, due 13 April 2016
Solutions: pdf, andR
code: tumor.R  Homework 3 for Part 3, due 20 April 2016
Data: malebachelors, malenone
Solutions: pdf, andR
code: birth_edu_male.R, logistic.R, and mixexp.R 
Homework 4 for Part 4, due 27 April 2016
Data: schools 1, 2, and 3; 2008 election
Solutions: pdf, andR
code: students.R, sens.R, and election_2008.R 
Homework 5 for Part 5, due 4 May 2016
Data: clouds
Solutions: pdf, andR
code: birth_edu_male_gibbs.R, clouds.R 
Homework 6 covering Part 6, due 11 May 2016
Data: ages, and swim times
Solutions: pdf, andR
code: swim.R, marriage.R 
Homework 7 for Part 7, due 25 May 2016
Data: homeworks, and bikes
Solutions: pdf, andR
code: homework.R, bicycle.R 
Homework 8 for Part 8, due 1 June 2016
Data: exponential/Pareto, and change point
Solutions: pdf, andR
code: exppareto.R, changept.R
Computing
The recommended language for this course is R
,
which can be obtained from CRAN.
Other languages such as MATLAB
are allowed but are not recommended.
Examples in lecture, and help in office hours, etc., will be exclusively in R
.
Below are some helpful R
resources:
 A quick R tutorial and accompanying code file
 The University offers
R
tutoring in the Regenstein library  Some helpful video tutorials and step by step guides
 R Studio is an excelent multiplatform graphical
interface to
R
which you will likely prefer to the default Windows/OSX GUI(s).  Instructions for changing
the default working directory for
R
on Windows