Perfecting MCMC Sampling: Recipes and Reservations
Abstract
Perfect sampling entered statistical computation with the striking promise that Markov chain Monte Carlo samplers could be modified to produce independent and identically distributed draws from the target distribution in a finite number of iterations. Although this ideal is still unattainable for many problems in Bayesian computation, the conceptual advances of perfect sampling continue to influence the development of modern MCMC methodology. This chapter reviews the central ideas and theoretical underpinnings of perfect sampling and shows connections with contemporary algorithmic and theoretical innovations inspired by its original vision.
Fields of Study & Subjects
Markov Chains and Monte Carlo Methods
Theoretical and Computational Physics
Statistical Methods and Bayesian Inference
Publication details
- Journal / source
- Chapman & Hall/CRC eBooks
- Pages
- 203–230
- Publication date
- 26 February 2026
- Language
- EN
- Document type
- Book chapter