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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