6 citations · 8 across the 10 of their papers we have counts for
4 papers · 1 filter
AutoStep: Locally adaptive involutive MCMC
Tiange Liu, Nikola Surjanovic, Miguel Biron-Lattes +2
Many common Markov chain Monte Carlo (MCMC) kernels can be formulated using a deterministic involutive proposal with a step size parameter. Selecting an appropriate step size is of…
Is Gibbs sampling faster than Hamiltonian Monte Carlo on GLMs?
Son Luu, Zuheng Xu, Nikola Surjanovic +3
The Hamiltonian Monte Carlo (HMC) algorithm is often lauded for its ability to effectively sample from high-dimensional distributions. In this paper we challenge the presumed domin…
Uniform Ergodicity of Parallel Tempering With Efficient Local Exploration
Nikola Surjanovic, Saifuddin Syed, Alexandre Bouchard-Côté +1
Non-reversible parallel tempering (NRPT) is an effective algorithm for sampling from target distributions with complex geometry, such as those arising from posterior distributions…
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…