70 citations · 369 across the 48 of their papers we have counts for
11 papers · 1 filter
Ensemble Rejection Sampling
George Deligiannidis, Arnaud Doucet, Sylvain Rubenthaler
We introduce Ensemble Rejection Sampling, a scheme for exact simulation from the posterior distribution of the latent states of a class of non-linear non-Gaussian state-space model…
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…
Bernoulli Race Particle Filters
Sebastian M Schmon, Arnaud Doucet, George Deligiannidis
When the weights in a particle filter are not available analytically, standard resampling methods cannot be employed. To circumvent this problem state-of-the-art algorithms replace…
Unbiased Smoothing using Particle Independent Metropolis-Hastings
Lawrence Middleton, George Deligiannidis, Arnaud Doucet +1
We consider the approximation of expectations with respect to the distribution of a latent Markov process given noisy measurements. This is known as the smoothing problem and is of…
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…
Unbiased Markov chain Monte Carlo for intractable target distributions
Lawrence Middleton, George Deligiannidis, Arnaud Doucet +1
Performing numerical integration when the integrand itself cannot be evaluated point-wise is a challenging task that arises in statistical analysis, notably in Bayesian inference f…
Large Sample Asymptotics of the Pseudo-Marginal Method
Sebastian M. Schmon, George Deligiannidis, Arnaud Doucet +1
The pseudo-marginal algorithm is a variant of the Metropolis--Hastings algorithm which samples asymptotically from a probability distribution when it is only possible to estimate u…
Controlled Sequential Monte Carlo
Jeremy Heng, Adrian N. Bishop, George Deligiannidis +1
Sequential Monte Carlo methods, also known as particle methods, are a popular set of techniques for approximating high-dimensional probability distributions and their normalizing c…
Exponential Ergodicity of the Bouncy Particle Sampler
George Deligiannidis, Alexandre Bouchard-Côté, Arnaud Doucet
Non-reversible Markov chain Monte Carlo schemes based on piecewise deterministic Markov processes have been recently introduced in applied probability, automatic control, physics a…
Which ergodic averages have finite asymptotic variance?
George Deligiannidis, Anthony Lee
We show that the class of functions for which ergodic averages of a reversible Markov chain have finite asymptotic variance is determined by the class of functions for…
The Correlated Pseudo-Marginal Method
George Deligiannidis, Arnaud Doucet, Michael K. Pitt
The pseudo-marginal algorithm is a popular variant of the Metropolis--Hastings scheme which allows us to sample asymptotically from a target probability density , when we are on…