11 citations · 25 across the 8 of their papers we have counts for
9 papers · 1 filter
High-dimensional Adaptive MCMC with Reduced Computational Complexity
Max Hird, Samuel Livingstone
We propose an adaptive MCMC method that learns a linear preconditioner which is dense in its off-diagonal elements but sparse in its parametrisation. Due to this sparsity, we achie…
A note on diffusive/random-walk behaviour in Metropolis--Hastings algorithms
Yuxin Liu, Peiyi Zhou, Samuel Livingstone
We prove a general result that if a Metropolis--Hastings algorithm has a proposal that is not geometrically ergodic and the acceptance rate approaches unity at a suitable rate as t…
Sampling algorithms in statistical physics: a guide for statistics and machine learning
Michael F. Faulkner, Samuel Livingstone
We discuss several algorithms for sampling from unnormalized probability distributions in statistical physics, but using the language of statistics and machine learning. We provide…
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…