1 citations · 1 across the 2 of their papers we have counts for
8 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…
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…
Random Partition Models for Microclustering Tasks
Brenda Betancourt, Giacomo Zanella, Rebecca C. Steorts
Traditional Bayesian random partition models assume that the size of each cluster grows linearly with the number of data points. While this is appealing for some applications, this…
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…
Scalable Importance Tempering and Bayesian Variable Selection
Giacomo Zanella, Gareth Roberts
We propose a Monte Carlo algorithm to sample from high dimensional probability distributions that combines Markov chain Monte Carlo and importance sampling. We provide a careful th…
Scalable inference for crossed random effects models
Omiros Papaspiliopoulos, Gareth O. Roberts, Giacomo Zanella
We analyze the complexity of Gibbs samplers for inference in crossed random effect models used in modern analysis of variance. We demonstrate that for certain designs the plain van…