Searched for: subject:"process"
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document
Bierkens, G.N.J.C. (author), Fearnhead, Paul (author), Roberts, Gareth (author)
Standard MCMC methods can scale poorly to big data settings due to the need to evaluate the likelihood at each iteration. There have been a number of approximate MCMC algorithms that use sub-sampling ideas to reduce this computational burden, but with the drawback that these algorithms no longer target the true posterior distribution. We...
journal article 2019
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Bierkens, G.N.J.C. (author), Bouchard-Côté, Alexandre (author), Doucet, Arnaud (author), Duncan, Andrew B. (author), Fearnhead, Paul (author), Lienart, Thibaut (author), Roberts, Gareth (author), Vollmer, Sebastian J. (author)
journal article 2018
document
Bierkens, G.N.J.C. (author), Roberts, Gareth (author)
In Turitsyn, Chertkov and Vucelja [Phys. D 240 (2011) 410-414] a nonreversible Markov Chain Monte Carlo (MCMC) method on an augmented state space was introduced, here referred to as Lifted Metropolis-Hastings (LMH). A scaling limit of the magnetization process in the Curie-Weiss model is derived for LMH, as well as for Metropolis-Hastings (MH...
journal article 2017
document
Bierkens, G.N.J.C. (author), Duncan, Andrew (author)
Markov chain Monte Carlo (MCMC) methods provide an essential tool in statistics for sampling from complex probability distributions. While the standard approach to MCMC involves constructing discrete-time reversible Markov chains whose transition kernel is obtained via the Metropolis-Hastings algorithm, there has been recent interest in...
journal article 2017
Searched for: subject:"process"
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