70 citations · 138 across the 11 of their papers we have counts for
13 papers
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…
Pseudo-marginal Inference for CTMCs on Infinite Spaces via Monotonic Likelihood Approximations
Miguel Biron-Lattes, Alexandre Bouchard-Côté, Trevor Campbell
Bayesian inference for Continuous-Time Markov Chains (CTMCs) on countably infinite spaces is notoriously difficult because evaluating the likelihood exactly is intractable. One way…
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,…
Slice Sampling for General Completely Random Measures
Peiyuan Zhu, Alexandre Bouchard-Côté, Trevor Campbell
Completely random measures provide a principled approach to creating flexible unsupervised models, where the number of latent features is infinite and the number of features that i…
Particle-Gibbs Sampling For Bayesian Feature Allocation Models
Alexandre Bouchard-Côté, Andrew Roth
Bayesian feature allocation models are a popular tool for modelling data with a combinatorial latent structure. Exact inference in these models is generally intractable and so prac…
Blang: Bayesian declarative modelling of general data structures and inference via algorithms based on distribution continua
Alexandre Bouchard-Côté, Kevin Chern, Davor Cubranic +5
Consider a Bayesian inference problem where a variable of interest does not take values in a Euclidean space. These "non-standard" data structures are in reality fairly common. The…