Simulation-based inference

Content for Tuesday, October 3, 2023

Content

  • Monte Carlo methods
  • Markov chains
  • Metropolis–Hastings algorithm

Learning objectives

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

  • understand how ordinary Monte Carlo and Markov chain Monte Carlo (MCMC) methods differ
  • implement a MHG algorithm to draw samples from the posterior
  • use output of MCMC to obtain estimates and standard errors

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.

  • Green et al. (2015) (overview of MCMC methods)

Slides

 View all slides in new window  Download PDF of all slides

References

Geyer, C. J. (2011). Introduction to Markov chain Monte Carlo. In Handbook of Markov chain Monte Carlo. CRC Press. https://doi.org/10.1201/b10905-3
Green, P. J., Łatuszyński, K., Pereyra, M., & Robert, C. P. (2015). Bayesian computation: A summary of the current state, and samples backwards and forwards. Statistics and Computing, 25(4), 835–862. https://doi.org/10.1007/s11222-015-9574-5