**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.

**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

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.

**OpenBUGS Tutorial:** BUGS_Lesson.odc (PDF Version)

**Basic Bayesian Computation in R:** R_Lesson.R

**Data Analysis Guide:** Data_Analysis_Guide.pdf

**Diasorin Data Analysis:** Diasorin_DA.pdf

**Poly-Aromatic Hydrocarbon Data Analysis:** PAH_DA.pdf

**Syllabus:** sp20syllabus.pdf

**Syllabus:** sp19syllabus.pdf

**Syllabus:** sp18syllabus.pdf

Office: SMLC 328

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