720 citations · 1.4k across the 27 of their papers we have counts for
4 papers · 1 filter
Metropolis-Hastings with Averaged Acceptance Ratios
Christophe Andrieu, Sinan Yıldırım, Arnaud Doucet +1
Markov chain Monte Carlo (MCMC) methods to sample from a probability distribution defined on a space consist of the simulation of realisations of Markov chain…
A general perspective on the Metropolis-Hastings kernel
Christophe Andrieu, Anthony Lee, Sam Livingstone
Since its inception the Metropolis-Hastings kernel has been applied in sophisticated ways to address ever more challenging and diverse sampling problems. Its success stems from the…
Nonreversible MCMC from conditional invertible transforms: a complete recipe with convergence guarantees
Achille Thin, Nikita Kotelevskii, Christophe Andrieu +3
Markov Chain Monte Carlo (MCMC) is a class of algorithms to sample complex and high-dimensional probability distributions. The Metropolis-Hastings (MH) algorithm, the workhorse of…
Subgeometric hypocoercivity for piecewise-deterministic Markov process Monte Carlo methods
Christophe Andrieu, Paul Dobson, Andi Q. Wang
We extend the hypocoercivity framework for piecewise-deterministic Markov process (PDMP) Monte Carlo established in [Andrieu et. al. (2018)] to heavy-tailed target distributions, w…