10 citations · 20 across the 12 of their papers we have counts for
4 papers · 1 filter
Optimal scaling of MCMC algorithms: the Hamiltonian approach
P. Dobson, J. M. Sanz-Serna, K. C. Zygalakis
We present a simple, yet general approach to study the scaling properties as the dimensionality of Metropolised MCMC sampling algorithms increases. The study relies on the symmetri…
Piecewise Deterministic Sampling for Constrained Distributions
Joël Tatang Demano, Paul Dobson, Konstantinos Zygalakis
In this paper, we propose a novel class of Piecewise Deterministic Markov Processes (PDMPs) that are designed to sample from probability distributions supported on a convex set…
Accelerated Bayesian imaging by relaxed proximal-point Langevin sampling
Teresa Klatzer, Paul Dobson, Yoann Altmann +3
This paper presents a new accelerated proximal Markov chain Monte Carlo methodology to perform Bayesian inference in imaging inverse problems with an underlying convex geometry. Th…
Efficient Bayesian computation for low-photon imaging problems
Savvas Melidonis, Paul Dobson, Yoann Altmann +2
This paper studies a new and highly efficient Markov chain Monte Carlo (MCMC) methodology to perform Bayesian inference in low-photon imaging problems, with particular attention to…