70 citations · 261 across the 11 of their papers we have counts for
4 papers · 1 filter
Non-Reversible Parallel Tempering: a Scalable Highly Parallel MCMC Scheme
Saifuddin Syed, Alexandre Bouchard-Côté, George Deligiannidis +1
Parallel tempering (PT) methods are a popular class of Markov chain Monte Carlo schemes used to sample complex high-dimensional probability distributions. They rely on a collection…
Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models
Alexandre Bouchard-Côté, Arnaud Doucet, Andrew Roth
This paper presents a new Markov chain Monte Carlo method to sample from the posterior distribution of conjugate mixture models. This algorithm relies on a flexible split-merge pro…
Divide-and-Conquer with Sequential Monte Carlo
Fredrik Lindsten, Adam M. Johansen, Christian A. Naesseth +4
We propose a novel class of Sequential Monte Carlo (SMC) algorithms, appropriate for inference in probabilistic graphical models. This class of algorithms adopts a divide-and-conqu…
Efficient Continuous-Time Markov Chain Estimation
Monir Hajiaghayi, Bonnie Kirkpatrick, Liangliang Wang +1
Many problems of practical interest rely on Continuous-time Markov chains~(CTMCs) defined over combinatorial state spaces, rendering the computation of transition probabilities, an…