Schedule overview

The schedule information on this page is subject to changes.

• Lab: Wednesdays, 9 am–noon, Kerckhoff B123

• Lecture: Mondays, 9–10 am, Broad 100

• TA recitation and homework help: Thursdays, 5–7 pm, B123 Kerckhoff

• Instructor office hours: Tuesdays, 2:30-3:30 pm, Kerkchoff B123

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Homework due dates

• Homework 1: due 5 pm, January 10

• Homework 2: due 5 pm, January 17

• Homework 3: due 5 pm, January 24

• Homework 4: due 5 pm, January 31

• Homework 5: due 5 pm, February 7

• Homework 6: due 5 pm, February 14

• Homework 7: due 5 pm, February 21

• Homework 8: due 5 pm, February 28

• Homework 9: due 5 pm, March 7

• Homework 10: due 5 pm, March 14

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Lesson exercise due dates

• Lesson exercise 1: due noon, January 14

• Lesson exercise 2: due noon, January 21

• Lesson exercise 3: due noon, January 28

• Lesson exercise 4: due noon, February 4

• Lesson exercise 5: due noon, February 11

• Lesson exercise 6: due noon, February 18

• Lesson exercise 7: due noon, February 25

• Lesson exercise 8: due noon, March 4

• Lesson exercise 9: due noon, March 11

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Weekly schedule

The lessons for each Wednesday must be read ahead of time and associated lesson exercise submitted by noon on the Tuesday before.

• Week 0

  • Lesson 00: Preparing for the course

• Week 1

  • M 01/06: Lesson 01: Probability and scientific logic (lecture)

  • W 01/08: Intuitive modeling (no reading)

• Week 2

  • M 01/13: Lesson 02: Introduction to Bayesian modeling (lecture)

  • W 01/15: Lesson 03: Plotting posteriors

  • W 01/15: Lesson 04: Marginalization by integration

  • W 01/15: Lesson 05: Conjugacy

• Week 3

  • M 01/20: No class; Martin Luther King Day

  • W 01/22: Lesson 06: Parameter estimation by optimization

• Week 4

  • M 01/27: Lesson 07: Introduction to Markov chain Monte Carlo (lecture)

  • M 01/27: Lesson 11: Display of MCMC samples (lecture)

  • W 01/29: Lesson 08: Introduction to MCMC with Stan

  • W 01/29: Lesson 09: Mixture models and label switching

  • W 01/29: Lesson 10: Variate-covariate models

• Week 5

  • M 02/03: Lesson 14: Collector’s box of distributions (lecture)

  • W 02/05: Lesson 12: Model building with prior predictive checks

  • W 02/05: Lesson 13: Posterior predictive checks

• Week 6

  • M 02/10: Lesson 17: Model comparison (lecture)

  • W 02/12: Lesson 15: MCMC diagnostics

  • W 02/12: Lesson 16: The Funnel of Hell and uncentering

• Week 7

  • M 02/17: Lesson 19: Hierarchical models (lecture)

  • W 02/19: Lesson 18: Model comparison in practice

• Week 8

  • M 02/24: No class; Presidents Day

  • W 02/26: Lesson 21: Principled workflows (lecture)

  • W 02/26: Lesson 20: Implementation of hierarchical models

• Week 9

  • M 03/03: Lesson 25: Variational inference

  • W 03/05: Lesson 22: Simulation-based calibration in practice

• Week 10

  • M 03/10: Lesson 26: Course wrap-up (lecture)

  • W 03/12: Work on final homework