Explaining the Perfect Sampler
By George Casella, M. Lavine, Christian P. Robert. 2001
69 citations recorded by the source corpus.
In 1996, Propp and Wilson introduced coupling from the past (CFTP), an algorithm for generating a sample from the exact stationary distribution of a Markov chain. In 1998, Fill proposed another so–called perfect sampling algorithm. These algorithms have enormous potential in Markov Chain Monte Carlo (MCMC) problems because they eliminate the need to monitor convergence and mixing of the chain. This article provides a brief introduction to the algorithms, with an emphasis on understanding rather than technical detail.
View Paper: https://www.semanticscholar.org/paper/e579283f79a6d8f689fbc4c967c19d0f55f4823b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Pierre E. Jacob
- John O'Leary
- Yves F. Atchadé
- Radford M. Neal
- Yoshua Bengio
- Ruslan Salakhutdinov
- Zoubin Ghahramani
- Kyunghyun Cho
Citations in the corpus
- MCMC Using Hamiltonian Dynamics (2011, background): “Also see [4] for an alternative proof based directly on the rejection-sampling argument, as well as for a numerical illustration.”
- Unbiased Markov chain Monte Carlo methods with couplings (2020, methodology): “More recently Jacob et al. (2019) removed the bias from conditional particle filters (Andrieu et al.”