Bayesian Methods and Modern Statistics, Spring 2019 (Combined Undergraduate/Graduate Course)

• Syllabus
• Google group
• Labs
• Practice Exercises for Exams

Other Resources

• Learning Github
• Review of R
• Undergrad Lecture Notes
• PhD Lecture Notes
• Extra Practice with Functions

Module 0: Introduction to Course

• Lecture 0: Intro to the Course
• Lab 1: Intro to R (Please go over the intro to R lab on your own as I will expect that you know this from the pre-reqs).

Module 1: History of Bayes/Intro to Bayes

• Lecture 1: History of Bayes
• Lecture 2: Intro to Bayes
• Lab 2: Intro to Bayes (Jan 16)

Module 2: Intro to Decision Theory

• Lecture 2: Intro to Decision Theory
• Lab 3: Intro to Decision Theory (Jan 23)

Module 3: Introduction to Normality

• Lecture 3: Intro to Normality

• Lab 4: Normal Gamma Conjugacy (Jan 30)

• Exam I: Thursday, February 7

Module 4: The Normal-Gamma Model

• Lecture 4: Normal-Gamma Model
• Lab 4: Normal Gamma Conjugacy (February 13)

Module 5: Non-informative, Default, Objective Bayes

• Lecture 5: Non-informative Bayes (February 20)

Module 6: Monte Carlo

• Lecture 6: Monte Carlo
• Lab 5: Intro to Rejection Sampling (February 27)

• Lab 6: Naive Monte Carlo Exercise (March 6)

• Exam II: March 7

Module 7: The Metropolis Algorithm

• Lecture 7: The Metropolis Algorithm

Module 8: Gibbs Sampling and Data Augmentation

• Lecture 8: The Two-stage Gibbs Sampler
• Lecture 9: The Multi-stage Gibbs Sampler

• Lab 7: Intro to Gibbs (March 20)

• Lecture 10: Data augmentation (part I)
• Lecture 11: Data augmentation (part II)
• Research lecture
• Lab 8: Gibbs sampling + data augmentation (March 27)

Module 9: Multivariate Normal and Missing Data

• Lecture 12: Multivariate Normal
• Lecture 13: Missing Data
• Lab 9: Multivariate Normal and Missing Data (April 3)

Module 10: Linear Regression

• Lecture 13: Linear Regression
• Lab 10: Linear Regression

Module 11: Model Selection

• Lecture 14: Model Selection
• Lab 11: Gibbs and Metropolis