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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
  • 4. Sampling with MCMC
  • 5. Inference with Stan
  • 6. Practice building and assessing Bayesian models
  • 7. Model comparison
  • 8. Hierarchical models
    • Homework 8.1: Hierarchical models are hiding in plain sight (20 pts)
    • Homework 8.2 [SOLO]: Hierarchical modeling of repeated experiments (100 pts)
    • Homework 8.3 [SOLO]: Determining dissociation constants (100 pts)
    • Homework 8.4 [SOLO]: Noise in gene expression (100 pts)
    • Homework 10.2a: Propose your own analysis (0 pts)
  • 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

8. Hierarchical models

Choose one of 8.2, 8.3, or 8.4 to complete as a solo problem. Problems 8.1 and 10.2a are due a week before the solo problem.

  • Homework 8.1: Hierarchical models are hiding in plain sight (20 pts)
  • Homework 8.2 [SOLO]: Hierarchical modeling of repeated experiments (100 pts)
  • Homework 8.3 [SOLO]: Determining dissociation constants (100 pts)
  • Homework 8.4 [SOLO]: Noise in gene expression (100 pts)
  • Homework 10.2a: Propose your own analysis (0 pts)
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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.



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