4 papers · 1 filter
A coupling-based approach to f-divergences diagnostics for Markov chain Monte Carlo
Adrien Corenflos, Hai-Dang Dau
A long-standing gap exists between the theoretical analysis of Markov chain Monte Carlo convergence, which is often based on statistical divergences, and the diagnostics used in pr…
Particle Gibbs without the Gibbs bit
Adrien Corenflos
Exact parameter and trajectory inference in state-space models is typically achieved by one of two methods: particle marginal Metropolis-Hastings (PMMH) or particle Gibbs (PGibbs).…
Auxiliary MCMC and particle Gibbs samplers for parallelisable inference in latent dynamical systems
Adrien Corenflos, Simo Särkkä
Sampling from the full posterior distribution of high-dimensional non-linear, non-Gaussian latent dynamical models presents significant computational challenges. While Particle Gib…
Debiasing Piecewise Deterministic Markov Process samplers using couplings
Adrien Corenflos, Matthew Sutton, Nicolas Chopin
Monte Carlo methods -- such as Markov chain Monte Carlo (MCMC) and piecewise deterministic Markov process (PDMP) samplers -- provide asymptotically exact estimators of expectations…