From the 1 of 4 linked papers with an AI index.
4 papers
Optimal scaling of MCMC algorithms: the Hamiltonian approach
P. Dobson, J. M. Sanz-Serna, K. C. Zygalakis
The paper develops a general Hamiltonian‑based framework to analyze how Metropolis‑Hastings MCMC algorithms should be scaled as the dimensionality of the target distribution grows,…
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 se…
Accelerated optimization algorithms and ordinary differential equations: the convex non Euclidean case
Paul Dobson, Jesus MarÃa 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…
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…