Lessons

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

Recitations

R1: Review of probability
R2: Choosing priors and review of optimization
R3: Just homework help
R4. Introduction to Hamiltonian Monte Carlo
R5: Bayesian model building
R6: MCMC using Caltech’s HPC
R7: Sampling discrete parameters with Stan
R8: Discussion of HW 10 project proposals
R9: Just homework help

Homework

1. Intuitive generative modeling
2. Analytical and graphical methods for analysis of the posterior
- Homework 2.1: Overwhelming a prior (45 pts)
- Homework 2.2: Exponential conjugate prior (55 pts)
3. Maximum a posteriori parameter estimation
4. Sampling with MCMC
5. Inference with Stan
6. MCMC with ion channels
7. Model comparison
8. Hierarchical models
9. Principled pipelines and/or VI and/or hierarchical modeling
10. The grand finale
11. Course feedback

Schedule

Schedule overview
Homework due dates
Lesson exercise due dates
Weekly schedule

Policies

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

Resources

Software
Reading/tutorials

BE/Bi 103 b

2. Analytical and graphical methods for analysis of the posterior
View page source

2. Analytical and graphical methods for analysis of the posterior

Homework 2.1: Overwhelming a prior (45 pts)
Homework 2.2: Exponential conjugate prior (55 pts)

Previous Next

Last updated on Mar 08, 2024.

© 2014–2024 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.