720 citations · 1.4k across the 27 of their papers we have counts for
10 papers · 1 filter
Comparison of Markov chains via weak Poincaré inequalities with application to pseudo-marginal MCMC
Christophe Andrieu, Anthony Lee, Sam Power +1
We investigate the use of a certain class of functional inequalities known as weak Poincaré inequalities to bound convergence of Markov chains to equilibrium. We show that this ena…
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…
Hypocoercivity of Piecewise Deterministic Markov Process-Monte Carlo
Christophe Andrieu, Alain Durmus, Nikolas Nüsken +1
In this work, we establish -exponential convergence for a broad class of Piecewise Deterministic Markov Processes recently proposed in the context of Markov Process M…
On the utility of Metropolis-Hastings with asymmetric acceptance ratio
Christophe Andrieu, Arnaud Doucet, Sinan Yıldırım +1
The Metropolis-Hastings algorithm allows one to sample asymptotically from any probability distribution . There has been recently much work devoted to the development of variant…