Course Schedule:

Week Content

 Week 1: Aug 28  

 Week 1: Aug 30

 Week 1: Sept 1

 Week 1 notes

 Mon. Quiz 1: Introduction & Course Overview

 Wed. Mechanics of Bayesian Statistics

 Fri. Philosophy of Bayesian Statistics: (Read Gelman's Philosophy and Practice of Bayesian Statistics)

 Week 2: Sept 4  

 Week 2: Sept 6

 Week 2: Sept 8

 Week 2 notes

 Mon. No Class (Labor Day)

 Wed. HW 1 due, (LaTex) (PDF) Quiz 2: Belief, Probability, and Exchangeability

 Fri. Belief, Probability, and Exchangeability

 Week 3: Sept 11  

 Week 3: Sept 13

 Week 3: Sept 15

 Week 3 notes

 Mon. Quiz 3, HW 2 due (LaTex) (PDF). Binomial Models

 Wed. Poisson and Exponential Family Models

 Fri. Priors

 Week 4: Sept 18  

 Week 4: Sept 20

 Week 4: Sept 22

 Week 4 notes

 Mon. Quiz 4, HW 3 due (LaTex) (PDF). Posterior Sampling.

 Wed. Monte Carlo Procedures

 Fri. The Normal Model

 Week 5: Sept 25

 Week 5: Sept 27

 Week 5: Sept 29 

 Week 5 notes

 Mon. Quiz 5, HW 4 due (LaTex) (PDF). Normal Model (cont). R code: (MonteCarlo Normal)

 Wed. MCMC with Normal Model. R code: (Gibbs Sampler)

 Fri. Demo: Intro to MCMC (HTML) (R Markdown)

 Week 6: Oct 2

 Week 6: Oct 4

 Week 6: Oct 6

 Week 6 notes

 Mon. Quiz 6, HW 5 due (LaTex) (PDF). MCMC

 Wed. MCMC Theory

 Fri. Demo: MCMC/JAGS (HTML) (R Markdown)

 Week 7: Oct 9

 Week 7: Oct 11

 Week 7: Oct 13

 Week 7 notes

 Mon. Quiz 7, HW 6 due (LaTex) (PDF). Multivariate Normal Distribution

 Wed. Inverse Wishart Distribution

 Fri. No class: MT ASA Chapter Meeting in Missoula, MT

 Week 8: Oct 16

 Week 8: Oct 18

 Week 8: Oct 20

 Mon. Quiz 8, HW 7 due (LaTex) (PDF).

 Wed. In class exam (2016 in class exam) (2016 take-home exam) (midtermbikes.csv) (bozemanhousing.csv)

 Fri. No class - work on midterm take-home (2017 take-home exam) (BozemanHousing2017exam.csv)

 Week 9: Oct 23

 Week 9: Oct 25

 Week 9: Oct 27 

 Week 9 notes

 Mon. Quiz 9, Midterm take-home due. Hierarchical Modeling.

 Wed. Hierarchical Modeling.

 Fri. Lab: Shrinkage and Stein's Paradox (LaTex) (PDF) (SteinData.csv)

 Week 10: Oct 30

 Week 10: Nov 1

 Week 10: Nov 3 

 Week 10 notes

 Mon. Quiz 10, HW 8 due (LaTex) (PDF).  Bayesian Regression

 Wed. Bayesian Regression

 Fri. Bayesian Regression

 Week 11: Nov 6

 Week 11: Nov 8

 Week 11: Nov 10 

 Week 11 Notes

 Mon. Quiz 11, HW 9 due (LaTex) (PDF). (SeattleHousing.csv) Bayesian Regression + Non conjugate priors and Metropolis-Hastings 

 Wed. Metropolis-Hastings (Advanced MCMC)

 Fri. No Class (Veteran's Day)

 Week 12: Nov 13

 Week 12: Nov 15

 Week 12: Nov 17 

  Week 12 - 13 Notes

 Mon. Quiz 12, Metropolis-Hastings. Final Description Due, write 1/2 page summary of data set + proposed methods or paper.

 Wed. Hierarchical Regression.

 Fri. Hierarchical Regression.

 Week 13: Nov 20

 Week 13: Nov 22

 Week 13: Nov 24

 Mon. Quiz 13, HW 10 due (LaTex) (PDF) (SeattleBinaryHousing.csv). Latent Variable Models.

 Wed. No Class (Thanksgiving Break)

 Fri. No Class (Thanksgiving Break)

 Week 14: Nov 27

 Week 14: Nov 29

 Week 14: Dec 1

  Week 14 Notes

 Mon. Quiz 14. Bayesian testing and point mass priors.

 Wed. State-Space Models (Bayesian Time Series)

 Fri. State-Space Models (Bayesian Time Series)

 Week 15: Dec 4

 Week 15: Dec 6

 Week 15: Dec 8 

 Mon. In Class Final. Take home final due (PDF). Course Lecture Notes (PDF)

 Wed. Class Presentations (Savannah, Jake, Daniel)

 Fri. Class Presentations (Julia, Jacob, Esther)

  Finals Week: Dec 14: 4-5:50

 Class Presentations. (David, Nnamdi, Steve, Walker, Eli, Christian, Priscilla)

Project Rubric (PDF)


STAT 532 Overview:

  • Meeting Time: Monday, Wednesday, Friday -  9:00 - 9:50 
  • Classroom: Wilson Hall 1-144
  • Office Hours: Monday 10 - 12

Course Description

This course will introduce the basic ideas of Bayesian statistics with emphasis on both philosophical foundations and practical implementation. The goal of this course is to provide a theoretical overview of Bayesian statistics and relevant computational tools along with the knowledge and experience to  use them in a research setting.


One of: STAT 422 or STAT 502 and STAT 506

Course Objectives

At the completion of this course, students will be able to:

  1. Describe fundamental differences between Bayesian and classical inference,
  2. Select appropriate models and priors, write likelihoods, and derive posterior distributions given a research question and dataset,
  3. Make inferences from posterior distributions,
  4. Implement Markov Chain Monte Carlo (MCMC) algorithms, and 
  5. Read, understand, and explain techniques in scientific journals implementing Bayesian methods.


  1. A First Course in Bayesian Methods, by Peter Hoff.

Course Evaluation:

  • Quizzes: 10% of final grade:
    • There is no formal attendance policy, but there will be weekly quizzes on Monday. 

  • Homework: 30% of final grade
    • Homework problems will be assigned every week. Students are allowed and encouraged to work with classmates on homework assignments, but each student is required to write their own homework.
  • Midterm Exam 20% & Final Exam 20%  of final grade
    • Exams will have two components: an in-class exam and a take home portion. The in-class portions will be largely conceptual including some short mathematical derivations. The take home portions will focus on the analysis of data and implementation of Bayesian computational methods.
  • Presentation 20% of final grade
    • There are two possibilities for the presentation:
      • A research-based approach where students will give a class presentation on a paper focused on Bayesian modeling and/or computation.
      • A case study approach where students will apply Bayesian methods to a dataset agreed upon by the instructor and student.