Schedule overview

The schedule information on this page is subject to changes.

• Lab

  • Section 1: Wednesdays, 9 am–noon, Broad 200

  • Section 2: Wednesdays, 1–4 pm, Chen 240a

• Lecture

  • Section 1: Mondays, 9–10 am, Broad 100

  • Section 2: Mondays, 10–11 am, Broad 100

• TA recitation: Thursdays, 7–8:30 pm, Chen 100

• TA homework help: Tuesdays, 2:30–4 pm, Broad 200

• Extra TA homework help: Thursdays, 8:30–10 pm, Chen 100

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

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Homework due dates

• Homework 1: due 5 pm, January 12

• Homework 2: due 5 pm, January 19

• Homework 3: due 5 pm, January 26

• Homework 4: due 5 pm, February 2

• Homework 5: due 5 pm, February 9

• Homework 6: due 5 pm, February 16

• Homework 7: due 5 pm, February 23

• Homework 8: due 5 pm, March 1

• Homework 9: due 5 pm, March 8

• Homework 10: due 4 pm, March 15

• Homework 11: due 5 pm, March 15

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Lesson exercise due dates

• Lesson exercise 1: due noon, January 9

• Lesson exercise 2: due noon, January 16

• Lesson exercise 3: due noon, January 23

• Lesson exercise 4: due noon, January 30

• Lesson exercise 5: due noon, February 6

• Lesson exercise 6: due noon, February 13

• Lesson exercise 7: due noon, February 20

• Lesson exercise 8: due noon, February 27

• Lesson exercise 9: due noon, March 5

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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

  • W 01/03: Lesson 01: Probability and scientific logic (lecture)

• Week 2

  • M 01/08: Lesson 02: Introduction to Bayesian modeling (lecture)

  • W 01/10: Lesson 03: Plotting posteriors

  • W 01/10: Lesson 04: Marginalization by integration

  • W 01/10: Lesson 05: Conjugacy

  • Th 01/11: Recitation 01: Probability review

• Week 3

  • M 01/15: No class; Martin Luther King Day

  • W 01/17: Lesson 07: Introduction to Markov chain Monte Carlo (lecture)

  • W 01/17: Lesson 06: Parameter estimation by optimization

  • Th 01/18: Recitation 02: Choosing priors and review of optimization

• Week 4

  • M 01/22: Lesson 11: Display of MCMC samples (lecture)

  • W 01/24: Lesson 08: Introduction to MCMC with Stan

  • W 01/24: Lesson 09: Mixture models and label switching

  • W 01/24: Lesson 10: Regression with Stan

  • Th 01/25: No recitation; only homework help

• Week 5

  • M 01/29: Lesson 14: Collector’s box of distributions (lecture)

  • W 01/31: Lesson 12: Model building with prior predictive checks

  • W 01/31: Lesson 13: Posterior predictive checks

  • Th 02/01: Recitation 04: Introduction to Hamiltonian Monte Carlo

• Week 6

  • M 02/05: Lesson 17: Model comparison (lecture)

  • W 02/07: Lesson 15: MCMC diagnostics

  • W 02/07: Lesson 16: The Funnel of Hell and uncentering

  • Th 02/08: Recitation 05: Practice model building

• Week 7

  • M 02/12: Lesson 19: Hierarchical models (lecture)

  • W 02/14: Lesson 18: Model comparison in practice

  • Th 02/15: Recitation 06: MCMC using Caltech’s HPC

• Week 8

  • M 02/19: No class; Presidents Day

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

  • W 02/21: Lesson 20: Implementation of hierarchical models

  • Th 02/22: Recitation 07: Sampling discrete parameters with Stan

• Week 9

  • M 02/26: Lesson 25: Variational inference

  • W 02/28: Lesson 22: Simulation-based calibration in practice

  • Th 02/29: Recitation 08: Discussion of HW 10 project proposals

• Week 10

  • M 03/04: Lesson 26: Course wrap-up (lecture)

  • W 03/06: Work on final homework

  • Th 03/08: No recitation; only homework help