720 citations · 1.4k across the 27 of their papers we have counts for
29 papers
Explicit convergence bounds for Metropolis Markov chains: isoperimetry, spectral gaps and profiles
Christophe Andrieu, Anthony Lee, Sam Power +1
We derive the first explicit bounds for the spectral gap of a random walk Metropolis algorithm on for any value of the proposal variance, which when scaled appropriately reco…
Poincaré inequalities for Markov chains: a meeting with Cheeger, Lyapunov and Metropolis
Christophe Andrieu, Anthony Lee, Sam Power +1
We develop a theory of weak Poincaré inequalities to characterize convergence rates of ergodic Markov chains. Motivated by the application of Markov chains in the context of algori…
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…