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Lesson |
Reading |
Video |
Slides |
Slides (pdf) |
Lab |
HW |
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Module 0.1: Course overview |
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| WEEK 1 |
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MODULE 1: INTRODUCTION TO BAYESIAN INFERENCE |
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| Mon, June 29 |
Module 1.1: Building blocks of Bayesian inference |
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Module 1.2: Probability review |
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Lab 1: R review |
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MODULE 2: ONE PARAMETER MODELS |
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| Tues, June 30 |
Module 2.1: Conjugacy; Beta-Bernoulli and beta-binomial models |
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Module 2.2: Operationalizing data analysis; selecting priors |
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Homework 1 |
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| Wed, July 1 |
Drop/Add for Term II ends |
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Module 2.3: Marginal likelihood and posterior prediction |
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Module 2.4: Truncated priors and the inverse cdf method |
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Lab 2: The Beta-Binomial model |
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| Thur, July 2 |
Module 2.5: Frequentist vs Bayesian intervals |
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Module 2.6: Loss functions and Bayes risk |
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| Fri, July 3 |
Independence Day holiday observed |
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| WEEK 2 |
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| Mon, July 6 |
Module 2.7: Gamma-Poisson model I |
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Module 2.8: Gamma-Poisson model II; finding conjugate distributions |
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Lab 3: The Poisson model and posterior predictive checks |
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| Tues, July 7 |
Quiz I |
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Homework 2 |
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MODULE 3: MONTE CARLO AND MULTIPARAMETER MODELS |
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| Wed, July 8 |
Module 3.1: Monte Carlo approximation and sampling |
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Module 3.2: Rejection sampling; Importance sampling |
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Lab 4: Prior selection and model reparameterization |
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| Thur, July 9 |
Module 3.3: The normal model: introduction and motivating examples |
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Module 3.4: The normal model: conditional inference for the mean |
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| Fri, July 10 |
Module 3.5: The normal model: joint inference for mean and variance |
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Module 3.5b: The normal model: joint inference for mean and variance (illustration) |
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Module 3.6: Noninformative and improper priors |
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| WEEK 3 |
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| Mon, July 13 |
Module 3.7: MCMC and Gibbs sampling I |
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Module 3.8: MCMC and Gibbs sampling II |
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Lab 5: Truncated data |
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| Tues, July 14 |
Module 3.9: MCMC and Gibbs sampling III |
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Module 3.10: MCMC and Gibbs sampling IV |
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Module 3.11: Discussion session exercisee |
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Homework 3 |
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MODULE 4: MULTIVARIATE DATA |
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| Wed, July 15 |
Module 4.1: Multivariate normal model I |
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Module 4.2: Multivariate normal model II |
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Lab 6: Gibbs sampling with block updates |
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| Thurs, July 16 |
Review for midterm exam |
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| Fri, July 17 |
Midterm exam |
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| WEEK 4 |
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| Mon, July 20 |
Module 4.3: Multivariate normal model III |
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Module 4.4: Multivariate normal model IV |
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Lab 7: Introduction to Hamiltonian Monte Carlo |
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| Tues, July 21 |
Module 4.5: Missing data and imputation I |
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Module 4.6: Missing data and imputation II |
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Homework 4 |
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MODULE 5: HIERARCHICAL MODELS |
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| Wed, July 22 |
Module 5.1: Hierarchical normal models with constant variance: two groups |
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Module 5.2: Hierarchical normal models with constant variance: two groups (illustration) |
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Module 5.3: Hierarchical normal models with constant variance: multiple groups |
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No lab |
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| Thurs, July 23 |
Module 5.4: Hierarchical normal modeling of means and variances |
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Module 5.5: Hierarchical normal modeling of means and variances (illustration) |
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MODULE 6: BAYESIAN LINEAR REGRESSION |
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| Fri, July 24 |
Module 6.1: Bayesian linear regression |
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Module 6.2: Bayesian linear regression (illustration) |
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| WEEK 5 |
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| Mon, July 27 |
Last day to withdraw with W |
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Module 6.3: Bayesian linear regression: weakly informative priors |
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Module 6.4: Bayesian hypothesis testing |
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Lab 8: Hierarchical modeling |
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| Tues, July 28 |
Module 6.5: Bayesian model selection |
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Module 6.6: Bayesian model selection (illustration) |
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Homework 5 |
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| Wed, July 29 |
Quiz II |
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Lab 9: Bayesian (Generalized) Linear Regression Models |
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MODULE 7: METROPOLIS AND METROPOLIS-HASTINGS |
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| Thurs, July 30 |
Module 7.1: The Metropolis algorithm |
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Module 7.2: Metropolis in action |
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| Fri, July 31 |
Module 7.3: The Metropolis-Hastings algorithm |
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Module 7.4: Metropolis within Gibbs |
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MODULE 8: CATEGORICAL DATA AND MIXTURE MODELS |
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| WEEK 6 |
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| Mon, Aug 3 |
Module 8.1: The multinomial model |
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Module 8.2: Finite mixture models: univariate categorical data |
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Lab 10: Metropolis-Hastings |
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| Tues, Aug 4 |
Module 8.3: Finite mixture models: univariate continuous data |
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Module 8.4: Finite mixture models: univariate continuous data (illustration) |
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| Wed, Aug 5 |
Module 8.5: Finite mixture models: multivariate categorical data |
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Module 8.6: Finite mixture models: multivariate continuous data |
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No lab |
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| Thurs, Aug 6 |
Course wrap-up and review for final exam |
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| Fri, Aug 7 - Sun, Aug 9 |
Final exam period |
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