11 citations · 21 across the 4 of their papers we have counts for
6 papers
Optimal design of the Barker proposal and other locally-balanced Metropolis-Hastings algorithms
Jure Vogrinc, Samuel Livingstone, Giacomo Zanella
We study the class of first-order locally-balanced Metropolis--Hastings algorithms introduced in Livingstone & Zanella (2021). To choose a specific algorithm within the class the u…
Adaptive random neighbourhood informed Markov chain Monte Carlo for high-dimensional Bayesian variable Selection
Xitong Liang, Samuel Livingstone, Jim Griffin
We introduce a framework for efficient Markov Chain Monte Carlo (MCMC) algorithms targeting discrete-valued high-dimensional distributions, such as posterior distributions in Bayes…
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…
A fresh take on 'Barker dynamics' for MCMC
Max Hird, Samuel Livingstone, Giacomo Zanella
We study a recently introduced gradient-based Markov chain Monte Carlo method based on 'Barker dynamics'. We provide a full derivation of the method from first principles, placing…
The Barker proposal: combining robustness and efficiency in gradient-based MCMC
Samuel Livingstone, Giacomo Zanella
There is a tension between robustness and efficiency when designing Markov chain Monte Carlo (MCMC) sampling algorithms. Here we focus on robustness with respect to tuning paramete…
Peskun-Tierney ordering for Markov chain and process Monte Carlo: beyond the reversible scenario
Christophe Andrieu, Samuel Livingstone
Historically time-reversibility of the transitions or processes underpinning Markov chain Monte Carlo methods (MCMC) has played a key rôle in their development, while the self-adjo…