We propose discrete Langevin proposal (DLP), a simple and scalable gradient-based
proposal for sampling complex high-dimensional discrete distributions. In contrast to Gibbs …

Optimal scaling has been well studied for Metropolis-Hastings (MH) algorithms in
continuous spaces, but a similar understanding has been lacking in discrete spaces …

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 …

Stochastic gradient descent is an optimisation method that combines classical gradient
descent with random subsampling within the target functional. In this work, we introduce the …

We propose a goodness-of-fit measure for probability densities modeling observations with
varying dimensionality, such as text documents of differing lengths or variable-length …

Stein importance sampling is a widely applicable technique based on kernelized Stein
discrepancy, which corrects the output of approximate sampling algorithms by reweighting …

Abstract Stochastic Gradient (SG) Markov Chain Monte Carlo algorithms (MCMC) are
popular algorithms for Bayesian sampling in the presence of large datasets. However, they …

Multiple-try Metropolis (MTM) is a popular Markov chain Monte Carlo method with the
appealing feature of being amenable to parallel computing. At each iteration, it samples …

We study the class of first-order locally balanced Metropolis–Hastings algorithms introduced
in Livingstone & Zanella (2022). To choose a specific algorithm within the class, the user …

We introduce a framework for efficient Markov chain Monte Carlo algorithms targeting
discrete-valued high-dimensional distributions, such as posterior distributions in Bayesian …