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Lessons

  • Lesson 0: Setting up computing resources
  • Lesson 1: Introduction to Bayesian modeling
  • Lesson 2: Parameter estimation by optimization
  • Lesson 3: Markov chain Monte Carlo and Stan
  • Lesson 4: Display of MCMC results and diagnostics
  • Lesson 5: Prior and posterior predictive checks
  • Lesson 6: Hierarchical models
  • Lesson 7: Principled pipelines
    • Principled analysis pipelines
    • Simulation based calibration and related checks in practice

Exercises

  • Exercise 1. Practice with Bayesian modeling
  • Exercise 2. Paremeter estimation by optimization
  • Exercise 3. First foray into MCMC
  • Exercise 4: More Bayesian inference with MCMC
  • Exercise 5: Bayesian modeling with prior and posterior predictive checks
  • Exercise 6: Hierarchical models
  • Exercise 7: Principled pipelines

Schedule

  • Schedule overview
  • Daily schedule
PoL workshop on statistical inference, part 2
  • Lesson 7: Principled pipelines
  • View page source

Lesson 7: Principled pipelines

  • Principled analysis pipelines
  • Simulation based calibration and related checks in practice
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Last updated on Aug 12, 2024.

© 2021–2024 Justin Bois. 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.


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