I teach an applied Bayesian statistics course at University of Guelph offered every other year. The course description is below:

Bayesian Data Analysis: STAT*4050/6920


This course will be an applied statistics course from the perspective of the Bayesian. We will introduce the Bayesian framework as an intuitive framework for statistical analysis, with an emphasis on modern applied techniques from a conceptual perspective. Topics in this course include:

·      The practical differences between Bayesian and frequentist statistics

·      Prior beliefs, updating, and statistical evidence

·      Bayesian hierarchical models

·      Bayesian computation

·      The Bayesian workflow – model building and checking

The main tool for analysis in this course will be Stan, a probabilistic programming language designed for Bayesian inference. No prior experience with Stan is assumed, but students should have experience with R or a similar language. I will assume that students have prior experience with statistical modelling (linear regression models and GLMs as seen in STAT*3240, STAT*3510, POPM*6210*01 or similar) and some experience with mathematical statistics (expectations, conditional probability, and probability distributions as seen in STAT*3100 or similar). Graduate students will have additional responsibilities.

Courses Taught:

STAT*4360/6920 Applied Bayesian Data Analysis - Winter 2024

STAT*4360 (Applied Time Series Analysis) at University of Guelph - Fall 2023

DATA*6500 (Analysis of Spatial-Temporal Data) at University of Guelph - Summer 2023, Summer 2024 

STA303 (Methods of Data Analysis II) at University of Toronto - Summer 2022 

STAB22 (Introductory Statistics) at University of Toronto - Fall 2021