BE/Bi 103 b: Statistical Inference in the Biological Sciences

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

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