18 citations · 77 across the 20 of their papers we have counts for
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Randomized Time Riemannian Manifold Hamiltonian Monte Carlo
Peter A. Whalley, Daniel Paulin, Benedict Leimkuhler
Hamiltonian Monte Carlo (HMC) algorithms which combine numerical approximation of Hamiltonian dynamics on finite intervals with stochastic refreshment and Metropolis correction are…
Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting
Maxime Vono, Daniel Paulin, Arnaud Doucet
Performing exact Bayesian inference for complex models is computationally intractable. Markov chain Monte Carlo (MCMC) algorithms can provide reliable approximations of the posteri…
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…