10 citations · 20 across the 12 of their papers we have counts for
15 papers
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 optimization algorithms and ordinary differential equations: the convex non Euclidean case
Paul Dobson, Jesus María Sanz-Serna, Konstantinos C. Zygalakis
We study the connections between ordinary differential equations and optimization algorithms in a non-Euclidean setting. We propose a novel accelerated algorithm for minimising con…
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…
On the connections between optimization algorithms, Lyapunov functions, and differential equations: theory and insights
Paul Dobson, Jesus Maria Sanz-Serna, Konstantinos Zygalakis
We revisit the general framework introduced by Fazylab et al. (SIAM J. Optim. 28, 2018) to construct Lyapunov functions for optimization algorithms in discrete and continuous time.…
Piecewise deterministic sampling with splitting schemes
Andrea Bertazzi, Paul Dobson, Pierre Monmarché
We introduce Markov chain Monte Carlo (MCMC) algorithms based on numerical approximations of piecewise-deterministic Markov processes obtained with the framework of splitting schem…