Bayesian workflow and model diagnostics

Content

  • Bayesian workflow
  • Computational tricks
  • Model diagnostics (WAIC, LOO-CV, etc.)

Learning objectives

At the end of the chapter, students should be able to

  • choose suitable test statistics to evaluate model adequacy
  • assess convergence using graphical tools and effective sample size
  • perform model comparisons using Bayes factor or predictive measures
  • diagnose performance of MCMC algorithms and implement potential remedies

Readings

Warning

These readings should be completed before class, to ensure timely understanding and let us discuss the concepts together through various examples and case studies — the strict minimum being the course notes.

Complementary readings

Warning

Complementary readings are additional sources of information that are not required readings, but may be useful substitutes. Sometimes, they go beyond the scope of what we cover and provide more details.

  • Gelman et al. (2013), chapters 6 and 7
  • McElreath (2020), chapter 9.5

Slides

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References

Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis (3rd ed.). Chapman; Hall/CRC. https://doi.org/10.1201/b16018
Gelman, A., Vehtari, A., Simpson, D., Margossian, C. C., Carpenter, B., Yao, Y., Kennedy, L., Gabry, J., Bürkner, P.-C., & Modrák, M. (2020). Bayesian workflow. arXiv. https://doi.org/https://doi.org/10.48550/arXiv.2011.01808
McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and STAN (2nd ed.). Chapman; Hall/CRC.