STATS 477 / 577 – Introduction to Bayesian Modeling

Introduction to Bayesian Modeling is a first course in applied Bayesian data analysis. Knowledge of probability and regression modeling is expected. Students are introduced to subjectivist notions of probability and how outside expert information can be incorporated into data analysis through informative prior distributions. As I teach it, this course is heavily focused on statistical thinking and data analysis—not on the deeper math associated with Bayesian inference and MCMC methods.

Course Information

Room: None, this class will be held online.

Time: Tuesday & Thursday, 11:00am – 12:15pm

Prerequisites: STATS 461 (Probability) and STATS 427 or 440 (Regression)

Syllabus: sp21syllabus.pdf

Course Materials

Kristin Lennox on Core Concepts

Dr. Kristin Lennox, formerly of Lawrence Livermore Nat'l Labs, discusses some of the core differences between Bayesian and Frequentist approaches to statistics and provides a historical overview of the development of these paradigms.

Computer Tutorials

OpenBUGS Tutorial: BUGS_Lesson.odc (PDF Version)

Basic Bayesian Computation in R: R_Lesson.R

Data Analysis – Guides and Examples

Data Analysis Guide: Data_Analysis_Guide.pdf

Diasorin Data Analysis: Diasorin_DA.pdf

Poly-Aromatic Hydrocarbon Data Analysis: PAH_DA.pdf



Midterm Exam
Final Project

Previous Terms

Spring 2020

Syllabus: sp20syllabus.pdf

Spring 2019

Syllabus: sp19syllabus.pdf

Spring 2018

Syllabus: sp18syllabus.pdf

Fletcher G.W. Christensen

Asst. Professor of Statistics

Office: SMLC 328

Spring 2020 Office Hours (Zoom):
  • Tuesday 2:00pm – 3:15pm