339 citations · 551 across the 39 of their papers we have counts for
8 papers · 1 filter
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…
Analysis of Langevin Monte Carlo via convex optimization
Alain Durmus, Szymon Majewski, Błażej Miasojedow
In this paper, we provide new insights on the Unadjusted Langevin Algorithm. We show that this method can be formulated as a first order optimization algorithm of an objective func…
Particle MCMC with Poisson Resampling: Parallelization and Continuous Time Models
Tomasz Cąkała, Błażej Miasojedow, Wojciech Niemiro
We introduce a new version of particle filter in which the number of "children" of a particle at a given time has a Poisson distribution. As a result, the number of particles is ra…
Geometric ergodicity of Rao and Teh's algorithm for Markov jump processes and CTBNs
Błażej Miasojedow, Wojcieh Niemiro
Rao and Teh (2012, 2013) introduced an efficient MCMC algorithm for sampling from the posterior distribution of a hidden Markov jump process. The algorithm is based on the idea of…
Particle Gibbs algorithms for Markov jump processes
Blazej Miasojedow, Wojciech Niemiro
In the present paper we propose a new MCMC algorithm for sampling from the posterior distribution of hidden trajectory of a Markov jump process. Our algorithm is based on the idea…
State dependent swap strategies and adaptive adjusting of number of temperatures in Parallel Tempering algorithms
Mateusz Krzysztof Łącki, Błażej Miasojedow
In this paper we present extensions to the original adaptive parallel tempering algorithm. Two different approaches are presented. In the first one we introduce state-dependent str…
Adaptive parallel tempering algorithm
Blazej Miasojedow, Eric Moulines, Matti Vihola
Parallel tempering is a generic Markov chain Monte Carlo sampling method which allows good mixing with multimodal target distributions, where conventional Metropolis-Hastings algor…
Nonasymptotic bounds on the mean square error for MCMC estimates via renewal techniques
Krzysztof Latuszynski, Blazej Miasojedow, Wojciech Niemiro
The Nummellin's split chain construction allows to decompose a Markov chain Monte Carlo (MCMC) trajectory into i.i.d. "excursions". RegenerativeMCMC algorithms based on this techni…