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Week Date Lesson Reading Video Slides Slides (pdf) Lab Problem Sets
WEEK 1 Thur, Jan 21 Module 0.1: Course overview and introduction
MODULE 1: INTRODUCTION TO BAYESIAN INFERENCE
WEEK 2 Mon, Jan 25 Lab 1: R review
Tue, Jan 26 Module 1.1: Building blocks of Bayesian inference
Module 1.2: Probability review
MODULE 2: ONE PARAMETER MODELS
Thur, Jan 28 Module 2.1: Conjugacy; Beta-Bernoulli and beta-binomial models
Module 2.2: Operationalizing data analysis; selecting priors
Fri, Jan 29 | NEW ASSIGNMENT: Homework 1
WEEK 3 Mon, Feb 1 Lab 2: The Beta-Binomial model
Tue, Feb 2 Module 2.3: Marginal likelihood and posterior prediction
Module 2.4: Truncated priors and the inverse cdf method
Drop/Add ends
Thur, Feb 4 Module 2.5: Frequentist vs Bayesian intervals
Module 2.6: Loss functions and Bayes risk
WEEK 4 Mon, Feb 8 Lab 3: The Poisson model and posterior predictive checks
Tue, Feb 9 Module 2.7: Gamma-Poisson model I
Module 2.8: Gamma-Poisson model II; finding conjugate distributions
Thur, Feb 11 Quiz I
Fri, Feb 12 | DELIVERABLES: Homework 1 due!
NEW ASSIGNMENT: Homework 2
MODULE 3: MONTE CARLO AND MULTIPARAMETER MODELS
WEEK 5 Mon, Feb 15 Lab 4: Prior selection and model reparameterization
Tue, Feb 16 Module 3.1: Monte Carlo approximation and sampling
Module 3.2: Rejection sampling; Importance sampling
Thur, Feb 18 Module 3.3: The normal model: introduction and motivating examples
Module 3.4: The normal model: conditional inference for the mean
Fri, Feb 19 | DELIVERABLES: Homework 2 due!
NEW ASSIGNMENT: Homework 3
WEEK 6 Mon, Feb 22 Lab 5: Truncated data
Tue, Feb 23 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
Thur, Feb 25 Module 3.7: MCMC and Gibbs sampling I
Module 3.8: MCMC and Gibbs sampling II
Fri, Feb 26 | DELIVERABLES: Homework 3 due!
NEW ASSIGNMENT: Homework 4
WEEK 7 Mon, Mar 1 No lab: mini-break
Tue, Mar 2 Module 3.9: MCMC and Gibbs sampling III
Module 3.10: MCMC and Gibbs sampling IV
Module 3.11: Discussion session exercise
MODULE 4: MULTIVARIATE DATA
Thur, Mar 4 Module 4.1: Multivariate normal model I
Module 4.2: Multivariate normal model II
Fri, Mar 5 | DELIVERABLES: Homework 4 due!
WEEK 8 Mon, Mar 8 No lab
Tue, Mar 9 No class
Thur, Mar 11 Review for midterm exam
WEEK 9 Mon, Mar 15 Midterm exam
Tue, Mar 16 Module 4.3: Multivariate normal model III
Module 4.4: Multivariate normal model IV
Thur, Mar 18 Module 4.5: Missing data and imputation I
Module 4.6: Missing data and imputation II
Fri, Mar 19 | NEW ASSIGNMENT: Homework 5
WEEK 10 Mon, Mar 22 Lab 6: Gibbs sampling with block updates
OPTIONAL MATERIAL: Lab 7: Introduction to Hamiltonian Monte Carlo
MODULE 5: HIERARCHICAL MODELS
Tue, Mar 23 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
Thur, Mar 25 Module 5.4: Hierarchical normal modeling of means and variances
Module 5.5: Hierarchical normal modeling of means and variances (illustration)
Fri, Mar 26 | NEW ASSIGNMENT: Homework 6
Sun, Mar 28 | DELIVERABLES: Homework 5 due!
WEEK 11 Mon, Mar 29 Lab 8: Hierarchical modeling
MODULE 6: BAYESIAN LINEAR REGRESSION
Tue, Mar 30 Module 6.1: Bayesian linear regression
Module 6.2: Bayesian linear regression (illustration)
Thur, Apr 1 Module 6.3: Bayesian linear regression: weakly informative priors
Module 6.4: Bayesian hypothesis testing
Fri, Apr 2 | NEW ASSIGNMENT: Homework 7
Sun, Apr 4 | DELIVERABLES: Homework 6 due!
WEEK 12 Mon, Apr 5 Lab 9: Bayesian (Generalized) Linear Regression Models
Tue, Apr 6 Module 6.5: Bayesian model selection
Module 6.6: Bayesian model selection (illustration)
Thur, Apr 8 Quiz II
Fri, Apr 9 | NEW ASSIGNMENT: Homework 8
Sun, Apr 11 | DELIVERABLES: Homework 7 due!
WEEK 13 Mon, Apr 12 Wellness day; no lab
MODULE 7: METROPOLIS AND METROPOLIS-HASTINGS
Tue, Apr 13 Module 7.1: The Metropolis algorithm
Module 7.2: Metropolis in action
Thur, Apr 15 Module 7.3: The Metropolis-Hastings algorithm
Module 7.4: Metropolis within Gibbs
Sun, Apr 18 | DELIVERABLES: Homework 8 due!
WEEK 14 Mon, Apr 19 Lab 10: Metropolis-Hastings
MODULE 8: CATEGORICAL DATA AND MIXTURE MODELS
Tue, Apr 20 Module 8.1: The multinomial model
Module 8.2: Finite mixture models: univariate categorical data
Thur, Apr 22 Module 8.3: Finite mixture models: univariate continuous data
Module 8.4: Finite mixture models: univariate continuous data (illustration)
Course wrap-up and review for final exam
Fri, Apr 23 | OPTIONAL MATERIAL: Module 8.5: Finite mixture models: multivariate categorical data
Module 8.6: Finite mixture models: multivariate continuous data
WEEK 15 Tue, Apr 27 Reading week; no class
Thur, Apr 29 Reading week; no class
Fri, Apr 30 - Sat, May 1 Final exam