70 citations · 108 across the 11 of their papers we have counts for
7 papers · 1 filter
Optimised Annealed Sequential Monte Carlo Samplers
Saifuddin Syed, Alexandre Bouchard-Côté, Kevin Chern +1
Annealed Sequential Monte Carlo (ASMC) samplers are special cases of SMC samplers where the sequence of distributions can be embedded in a smooth path of distributions. Using this…
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…
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…
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…
Parallel Tempering With a Variational Reference
Nikola Surjanovic, Saifuddin Syed, Alexandre Bouchard-Côté +1
Sampling from complex target distributions is a challenging task fundamental to Bayesian inference. Parallel tempering (PT) addresses this problem by constructing a Markov chain on…
Parallel Tempering on Optimized Paths
Saifuddin Syed, Vittorio Romaniello, Trevor Campbell +1
Parallel tempering (PT) is a class of Markov chain Monte Carlo algorithms that constructs a path of distributions annealing between a tractable reference and an intractable target,…
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…