Markov chain Monte Carlo

Content

  • Metropolis-adjusted Langevin algorithm (MALA)
  • Gibbs sampling
  • Data augmentation

Learning objectives

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

  • implement Gibbs sampling
  • derive the conditional distributions of a model for Gibbs sampling

Readings

  • Chapters 5 and 6 of the course notes

Complementary readings

  • Albert (2009), chapters 6 and 10 (several examples)
  • McElreath (2020), chapter 9 (non technical)

Slides

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References

Albert, J. (2009). Bayesian computation with R (2nd ed.). Springer. https://doi.org/10.1007/978-0-387-92298-0
McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and STAN (2nd ed.). Chapman; Hall/CRC.