6 citations · 11 across the 6 of their papers we have counts for
6 papers
Hessian-informed, Coordinate Friendly Hamiltonian Monte Carlo in Linear Time
Son Luu, Nikola Surjanovic, Zuheng Xu +2
Riemannian Hamiltonian Monte Carlo (RHMC) is a promising MCMC methodology thanks to its ability to accommodate position-dependent preconditioning and multi-step proposals. While RH…
MCMC-driven learning
Alexandre Bouchard-Côté, Trevor Campbell, Geoff Pleiss +1
This paper is intended to appear as a chapter for the Handbook of Markov Chain Monte Carlo. The goal of this chapter is to unify various problems at the intersection of Markov chai…
autoMALA: Locally adaptive Metropolis-adjusted Langevin algorithm
Miguel Biron-Lattes, Nikola Surjanovic, Saifuddin Syed +2
Selecting the step size for the Metropolis-adjusted Langevin algorithm (MALA) is necessary in order to obtain satisfactory performance. However, finding an adequate step size for a…
Automatic Regenerative Simulation via Non-Reversible Simulated Tempering
Miguel Biron-Lattes, Trevor Campbell, Alexandre Bouchard-Côté
Simulated Tempering (ST) is an MCMC algorithm for complex target distributions that operates on a path between the target and a more amenable reference distribution. Crucially, if…
Pigeons.jl: Distributed Sampling From Intractable Distributions
Nikola Surjanovic, Miguel Biron-Lattes, Paul Tiede +3
We introduce a software package, Pigeons.jl, that provides a way to leverage distributed computation to obtain samples from complicated probability distributions, such as multimoda…
Randomized Hamiltonian Monte Carlo as Scaling Limit of the Bouncy Particle Sampler and Dimension-Free Convergence Rates
George Deligiannidis, Daniel Paulin, Alexandre Bouchard-Côté +1
The Bouncy Particle Sampler is a Markov chain Monte Carlo method based on a nonreversible piecewise deterministic Markov process. In this scheme, a particle explores the state spac…