Lessons

1. Preparing your computer
2. Analytical approaches to Bayesian parameter estimation
3. Parameter estimation by optimization
4. Introduction to MCMC with Stan
5. Prior and posterior checks
6. MCMC diagnostics
7. Model comparison
8. Implementation of hierarchical models
9. Simulation based calibration in practice

Lecture notes

1. Probability and the logic of scientific reasoning
2. Introduction to Bayesian modeling
3. Introduction to Markov chain Monte Carlo
4. Display of MCMC results
5. Collector’s box of distributions
6. Model comparison
7. Hierarchical models
8. Principled work flows
- Principled analysis pipelines

Homework

1. Review of BE/Bi 103 a
2. Analytical and graphical methods for analysis of the posterior
3. Maximum a posteriori parameter estimation
4. Sampling with MCMC
5. Inference with Stan I
6. Practice building and assessing Bayesian models
7. Model comparison
8. Hierarchical models
9. Principled pipelines and hierarchical modeling of noise
10. The grand finale
11. Course feedback

Recitations

1. Review of Maximum Likelihood Estimation
3. Choosing priors
4. Getting AWS up and running
5. Modeling start to finish: Ant traffic
6. Practice modeling
9. Sampling discrete parameters

BE/Bi 103 b

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8. Principled work flows¶

Principled analysis pipelines

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Last updated on Mar 13, 2020.

© 2020 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. 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.

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