15 citations · 24 across the 5 of their papers we have counts for
6 papers
Solidarity of Gibbs Samplers: the spectral gap
Iwona Chlebicka, Krzysztof Łatuszyński, Błażej Miasojedow
Gibbs samplers are preeminent Markov chain Monte Carlo algorithms used in computational physics and statistical computing. Yet, their most fundamental properties, such as relations…
Efficient Bernoulli factory MCMC for intractable posteriors
Dootika Vats, Flávio Gonçalves, Krzysztof Łatuszyński +1
Accept-reject based Markov chain Monte Carlo (MCMC) algorithms have traditionally utilised acceptance probabilities that can be explicitly written as a function of the ratio of the…
A Framework for Adaptive MCMC Targeting Multimodal Distributions
Emilia Pompe, Chris Holmes, Krzysztof Łatuszyński
We propose a new Monte Carlo method for sampling from multimodal distributions. The idea of this technique is based on splitting the task into two: finding the modes of a target di…
Discussion of "Sequential Quasi-Monte-Carlo Sampling" by M. Gerber and N. Chopin
M. Pollock, A. M. Johansen, K. Łatuszyński +1
In this comment we consider whether QMC methods can be further embedded within SMC schemes in settings in which the transition density of the latent process is intractable and pseu…
Bayesian computation: a perspective on the current state, and sampling backwards and forwards
Peter J. Green, Krzysztof Łatuszyński, Marcelo Pereyra +1
The past decades have seen enormous improvements in computational inference based on statistical models, with continual enhancement in a wide range of computational tools, in compe…
Perfect simulation using atomic regeneration with application to Sequential Monte Carlo
Anthony Lee, Arnaud Doucet, Krzysztof Łatuszyński
Consider an irreducible, Harris recurrent Markov chain of transition kernel Π and invariant probability measure π. If Π satisfies a minorization condition, then the split chain all…