120 citations · 568 across the 33 of their papers we have counts for
5 papers · 1 filter
The True Cost of Stochastic Gradient Langevin Dynamics
Tigran Nagapetyan, Andrew B. Duncan, Leonard Hasenclever +3
The problem of posterior inference is central to Bayesian statistics and a wealth of Markov Chain Monte Carlo (MCMC) methods have been proposed to obtain asymptotically correct sam…
Using Perturbed Underdamped Langevin Dynamics to Efficiently Sample from Probability Distributions
A. B. Duncan, N. Nuesken, G. A. Pavliotis
In this paper we introduce and analyse Langevin samplers that consist of perturbations of the standard underdamped Langevin dynamics. The perturbed dynamics is such that its invari…
Note on A. Barbour's paper on Stein's method for diffusion approximations
Mikolaj J. Kasprzak, Andrew B. Duncan, Sebastian J. Vollmer
In (Barbour, 1990) foundations for diffusion approximation via Stein's method are laid. This paper has been cited more than 130 times and is a cornerstone in the area of Stein's me…
Nonreversible Langevin Samplers: Splitting Schemes, Analysis and Implementation
A. B. Duncan, G. A. Pavliotis, K. C. Zygalakis
For a given target density, there exist an infinite number of diffusion processes which are ergodic with respect to this density. As observed in a number of papers, samplers based…
Piecewise Deterministic Markov Processes for Scalable Monte Carlo on Restricted Domains
Joris Bierkens, Alexandre Bouchard-Côté, Arnaud Doucet +5
Piecewise Deterministic Monte Carlo algorithms enable simulation from a posterior distribution, whilst only needing to access a sub-sample of data at each iteration. We show how th…