Particle Markov Chain Monte Carlo

Particle systems as MCMC proposals

Particle filters inside general MCMC samplers. Darren Wilkinson wrote a series of blog posts introducing this idea:

Turns out to be especially natural for, e.g. change point problems.


Andrieu, Christophe, Arnaud Doucet, and Roman Holenstein. 2010. β€œParticle Markov Chain Monte Carlo Methods.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 72 (3): 269–342.
Chopin, Nicolas, and Sumeetpal S. Singh. 2015. β€œOn Particle Gibbs Sampling.” Bernoulli 21 (3).
Devlin, Lee, Paul Horridge, Peter L Green, and Simon Maskell. 2021. β€œThe No-U-Turn Sampler as a Proposal Distribution in a Sequential Monte Carlo Sampler with a Near-Optimal L-Kernel,” 5.
Godsill, Simon J, Arnaud Doucet, and Mike West. 2004. β€œMonte Carlo Smoothing for Nonlinear Time Series.” Journal of the American Statistical Association 99 (465): 156–68.
Lindsten, Fredrik, Michael I. Jordan, and Thomas B. SchΓΆn. 2014. β€œParticle Gibbs with Ancestor Sampling.” arXiv:1401.0604 [Stat], January.
Lindsten, Fredrik, and Thomas B. SchΓΆn. 2012. β€œOn the Use of Backward Simulation in the Particle Gibbs Sampler.” In 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3845–48.
Salomone, Robert, Leah F. South, Christopher C. Drovandi, and Dirk P. Kroese. 2018. β€œUnbiased and Consistent Nested Sampling via Sequential Monte Carlo,” May.
Whiteley, Nick, Christophe Andrieu, and Arnaud Doucet. 2010. β€œEfficient Bayesian Inference for Switching State-Space Models Using Discrete Particle Markov Chain Monte Carlo Methods.” arXiv:1011.2437 [Stat], November.

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