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How is latest development of particle markov chain monte carlo?

Variable-order Markov model

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Monte Carlo method in statistical physics

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The latest development in Particle Markov Chain Monte Carlo (PMCMC) includes the introduction of advanced algorithms that provide powerful methods for joint Bayesian state and parameter inference in nonlinear/non-Gaussian state-space models . These methods have been shown to have improved mixing rates and are suitable for distributed and multi-core architectures . Additionally, there have been developments in scaling up PMCMC by expressing the target density in terms of basic uniform or standard normal random numbers, resulting in a more efficient hybrid sampler . These advancements have allowed for the analysis of larger and more complex models with a reduced computational burden.

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Open access•Posted Content On Scalable Particle Markov Chain Monte Carlo David Gunawan, Chris Carter, Robert Kohn - Show less +2 more 12 Apr 2018-arXiv: Methodology 4 Citations	The paper discusses a scalable approach to Particle Markov Chain Monte Carlo (PMCMC) that improves efficiency in Bayesian inference for non-linear and non-Gaussian state space models.
Reference Entry•DOI Markov Chain Monte Carlo, Recent Developments Kerrie Mengersen 15 Jul 2005 2 Citations	The paper does not mention the latest development of particle Markov chain Monte Carlo. The paper discusses various MCMC algorithms and their applications in biostatistics.
Open access•Posted Content Ecient Particle Markov Chain Monte Carlo { Bayesian inference with a couple of particles Fredrik Lindsten, Thomas B. Schön - Show less +1 more 13 Oct 2011 1 Citations	The paper suggests alternatives to existing Particle Markov chain Monte Carlo (PMCMC) methods that are more robust to a low number of particles and a large number of observations.
Open access•Proceedings Article Interacting particle Markov chain Monte Carlo Tom Rainforth, Christian A. Naesseth, Fredrik Lindsten, Brooks Paige, Jan-Willem van de Meent, Arnaud Doucet, Frank Wood - Show less +6 more 19 Jun 2016 12 Citations	The paper introduces a new method called interacting particle Markov chain Monte Carlo (iPMCMC) which shows significant improvements in mixing rates compared to other PMCMC methods.
Open access•Posted Content On particle Gibbs Markov chain Monte Carlo models Pierre Del Moral, Robert Kohn, Fédéric Patras - Show less +2 more 23 Apr 2014-arXiv: Probability 13 Citations	The paper discusses a new class of advanced particle Markov chain Monte Carlo algorithms and provides a new perspective on their foundations and mathematical analysis. It also presents quantitative estimates of the convergence properties of these models.

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