Lessons

0. Preparing for the course
1. Probability and the logic of scientific reasoning
2. Plotting posteriors
3. Marginalization by numerical quadrature
4. Conjugacy
E1. To be completed after lesson 4
5. Introduction to Bayesian modeling
6. Parameter estimation by optimization
E2. To be completed after lesson 6
7. Introduction to Markov chain Monte Carlo
8. Cloud computing setup and usage
9. Introduction to MCMC with Stan
10. Mixture models and label switching with MCMC
11. Regression with MCMC
E3. To be completed after lesson 11
12. Display of MCMC results
13. Model building with prior predictive checks
14. Posterior predictive checks
E4. To be completed after lesson 14
15. Collector’s box of distributions
16. MCMC diagnostics
17. A diagnostics case study: Artificial funnel of hell
E5. To be completed after lesson 17
18. Model comparison
19. Model comparison in practice
E6. To be completed after lesson 19
20. Hierarchical models
21. Implementation of hierarchical models
E7. To be completed after lesson 21
22. Principled analysis pipelines
23: Simulation based calibration and related checks in practice
E8. To be completed after lesson 23
24. Introduction to Gaussian processes
25. Implementation of Gaussian processes
E9. To be completed after lesson 25
26: Variational Bayesian inference
27: Wrap-up

Recitations

R1: Review of probability
R2. Review of MLE
R3. Choosing priors
R4. Stan installation and use of AWS
R5. A Bayesian modeling case study: Ant traffic jams
R6. Practice model building
R7. Introduction to Hamiltonian Monte Carlo
R8: Discussion of HW 10 project proposals
R9: Sampling discrete parameters with Stan

Homework

0. Configuring your team
1. Intuitive generative modeling
2. Analytical and graphical methods for analysis of the posterior
3. Maximum a posteriori parameter estimation
- Homework 3.1 [SOLO]: Least squares (20 pts)
- Homework 3.2: MAP estimates and zero-inflation for Drop-Seq controls (80 pts)
4. Sampling with MCMC
5. Inference with Stan
6. Practice building and assessing Bayesian models
7. Model comparison
8. Hierarchical models
9. Principled pipelines
10. The grand finale
11. Course feedback

Schedule

Schedule overview
Homework due dates
Lesson exercise due dates
Weekly schedule

Policies

Meetings
Lab sessions
Lessons and lesson exercises
The BE/Bi 103 GitHub group
Homework
Grading
Collaboration policy and Honor Code
Excused absences and extensions
Course communications
“Ediquette”

Resources

Software
Reading/tutorials

BE/Bi 103 b

Archive of the Winter 2022 edition

View page source

3. Maximum a posteriori parameter estimation

This homework is due at 5 pm PST on January 28, 2022.

Homework 3.1 [SOLO]: Least squares (20 pts)
Homework 3.2: MAP estimates and zero-inflation for Drop-Seq controls (80 pts)

Previous Next

Last updated on Jan 02, 2024.

© 2021 Justin Bois and BE/Bi 103 b course staff. With the exception of pasted graphics, where the source is noted, this work is licensed under a Creative Commons Attribution License CC-BY 4.0 license. All code contained herein is licensed under an MIT license.

This document was prepared at Caltech with financial support from the Donna and Benjamin M. Rosen Bioengineering Center.

Built with Sphinx using a theme provided by Read the Docs.