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Week	Date	Lesson	Reading	Video	Slides	Slides (pdf)	Lab	Problem Sets
		Module 0.1: Course overview
	WEEK 1
		MODULE 1: INTRODUCTION TO BAYESIAN INFERENCE
	Wed, May 12	Module 1.1: Building blocks of Bayesian inference
		Module 1.2: Probability review
		Lab 1: R review
		MODULE 2: ONE PARAMETER MODELS
	Thurs, May 13	Module 2.1: Conjugacy; Beta-Bernoulli and beta-binomial models
		Module 2.2: Operationalizing data analysis; selecting priors
		Homework 1
	Fri, May 14	Drop/Add for Term I ends
		Module 2.3: Marginal likelihood and posterior prediction
		Module 2.4: Truncated priors and the inverse cdf method
		Lab 2: The Beta-Binomial model
	WEEK 2
	Mon, May 17	Module 2.5: Frequentist vs Bayesian intervals
		Module 2.6: Loss functions and Bayes risk
	Tues, May 18	Module 2.7: Gamma-Poisson model I
		Module 2.8: Gamma-Poisson model II; finding conjugate distributions
		MODULE 3: MONTE CARLO AND MULTIPARAMETER MODELS
	Wed, May 19	Module 3.1: Monte Carlo approximation and sampling
		Module 3.2: Rejection sampling; Importance sampling
		Lab 3: The Poisson model and posterior predictive checks
	Thurs, May 20	Quiz I
		Homework 2
	Fri, May 21	Module 3.3: The normal model: introduction and motivating examples
		Module 3.4: The normal model: conditional inference for the mean
		Lab 4: Prior selection and model reparameterization
	WEEK 3
	Mon, May 24	Module 3.5: The normal model: joint inference for mean and variance
		Module 3.5b: The normal model: joint inference for mean and variance (illustration)
		Module 3.6: Noninformative and improper priors
	Tues, May 25	Module 3.7: MCMC and Gibbs sampling I
		Module 3.8: MCMC and Gibbs sampling II
	Wed, May 26	Module 3.9: MCMC and Gibbs sampling III
		Module 3.10: MCMC and Gibbs sampling IV
		Module 3.11: Discussion session exercise
		Lab 5: Truncated data
		MODULE 4: MULTIVARIATE DATA
	Thurs, May 27	Module 4.1: Multivariate normal model I
		Module 4.2: Multivariate normal model II
		Homework 3
	Fri, May 28	Review for midterm exam
		Lab 6: Gibbs sampling with block updates
	WEEK 4
	Mon, May 31	Memorial day (no classes held)
	Tues, June 1	Midterm exam
	Wed, June 2	Module 4.3: Multivariate normal model III
		Module 4.4: Multivariate normal model IV
		Lab 7: Introduction to Hamiltonian Monte Carlo
	Thurs, June 3	Module 4.5: Missing data and imputation I
		Module 4.6: Missing data and imputation II
		Homework 4
		MODULE 5: HIERARCHICAL MODELS
	Fri, June 4	Module 5.1: Hierarchical normal models with constant variance: two groups
		Module 5.2: Hierarchical normal models with constant variance: two groups (illustration)
		Module 5.3: Hierarchical normal models with constant variance: multiple groups
		Lab 8: Hierarchical modeling
	WEEK 5
	Mon, June 7	Module 5.4: Hierarchical normal modeling of means and variances
		Module 5.5: Hierarchical normal modeling of means and variances (illustration)
		MODULE 6: BAYESIAN LINEAR REGRESSION
	Tues, June 8	Module 6.1: Bayesian linear regression
		Module 6.2: Bayesian linear regression (illustration)
	Wed, June 9	Last day to withdraw with W
		Module 6.3: Bayesian linear regression: weakly informative priors
		Module 6.4: Bayesian hypothesis testing
		Lab 9: Bayesian (generalized) linear regression models
	Thurs, June 10	Module 6.5: Bayesian model selection
		Module 6.6: Bayesian model selection (illustration)
		Homework 5
	Fri, June 11	Quiz II
		No lab
	WEEK 6
		MODULE 7: METROPOLIS AND METROPOLIS-HASTINGS
	Mon, June 14	Module 7.1: The Metropolis algorithm
		Module 7.2: Metropolis in action
	Tues, June 15	Module 7.3: The Metropolis-Hastings algorithm
		Module 7.4: Metropolis within Gibbs
		MODULE 8: CATEGORICAL DATA AND MIXTURE MODELS
	Wed, June 16	Module 8.1: The multinomial model
		Module 8.2: Finite mixture models: univariate categorical data
		Lab 10: Metropolis-Hastings
	Thur, June 17	Module 8.3: Finite mixture models: univariate continuous data
		Module 8.4: Finite mixture models: univariate continuous data (illustration)
	Fri, June 18	Module 8.5: Finite mixture models: multivariate categorical data
		Module 8.6: Finite mixture models: multivariate continuous data
		No lab
	WEEK 7
	Mon, June 21	Course wrap-up and review for final exam
	Wed, June 23 - Thurs June 24	Final exam period