70 citations · 261 across the 11 of their papers we have counts for
12 papers
Analysis of high-dimensional Continuous Time Markov Chains using the Local Bouncy Particle Sampler
Tingting Zhao, Alexandre Bouchard-Côté
Sampling the parameters of high-dimensional Continuous Time Markov Chains (CTMC) is a challenging problem with important applications in many fields of applied statistics. In this…
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…
Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets
Robert Cornish, Paul Vanetti, Alexandre Bouchard-Côté +2
Bayesian inference via standard Markov Chain Monte Carlo (MCMC) methods is too computationally intensive to handle large datasets, since the cost per step usually scales like $Θ(n)…
An Annealed Sequential Monte Carlo Method for Bayesian Phylogenetics
Liangliang Wang, Shijia Wang, Alexandre Bouchard-Côté
We describe an "embarrassingly parallel" method for Bayesian phylogenetic inference, annealed Sequential Monte Carlo, based on recent advances in the Sequential Monte Carlo literat…
Unbounded Bayesian Optimization via Regularization
Bobak Shahriari, Alexandre Bouchard-Côté, Nando de Freitas
Bayesian optimization has recently emerged as a popular and efficient tool for global optimization and hyperparameter tuning. Currently, the established Bayesian optimization pract…
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…
An Entropy Search Portfolio for Bayesian Optimization
Bobak Shahriari, Ziyu Wang, Matthew W. Hoffman +2
Bayesian optimization is a sample-efficient method for black-box global optimization. How- ever, the performance of a Bayesian optimization method very much depends on its explorat…
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…
A Note on Probabilistic Models over Strings: the Linear Algebra Approach
Alexandre Bouchard-Côté
Probabilistic models over strings have played a key role in developing methods allowing indels to be treated as phylogenetically informative events. There is an extensive literatur…
Evolutionary Inference via the Poisson Indel Process
Alexandre Bouchard-Côté, Michael I. Jordan
We address the problem of the joint statistical inference of phylogenetic trees and multiple sequence alignments from unaligned molecular sequences. This problem is generally formu…
Optimization of Structured Mean Field Objectives
Alexandre Bouchard-Cote, Michael I. Jordan
In intractable, undirected graphical models, an intuitive way of creating structured mean field approximations is to select an acyclic tractable subgraph. We show that the hardness…