ABSTRACT Statisticians often use Monte Carlo methods to approximate probability
distributions, primarily with Markov chain Monte Carlo and importance sampling. Sequential …

A core problem in statistics and probabilistic machine learning is to compute probability
distributions and expectations. This is the fundamental problem of Bayesian statistics and …

Abstract Annealed Importance Sampling (AIS) and its Sequential Monte Carlo (SMC)
extensions are state-of-the-art methods for estimating normalizing constants of probability …

We obtain several quantitative bounds on the mixing properties of an “ideal” Hamiltonian
Monte Carlo (HMC) Markov chain for a strongly log-concave target distribution π on R d. Our …

Antimicrobial resistance (AMR) emerges when disease-causing microorganisms develop
the ability to withstand the effects of antimicrobial therapy. This phenomenon is often fueled …

A standard way to move particles in a sequential Monte Carlo (SMC) sampler is to apply
several steps of a Markov chain Monte Carlo (MCMC) kernel. Unfortunately, it is not clear …

Sampling from a complex distribution $\pi $ and approximating its intractable normalizing
constant $\mathrm {Z} $ are challenging problems. In this paper, a novel family of …

Importance sampling (IS) is a powerful Monte Carlo (MC) methodology for approximating
integrals, for instance in the context of Bayesian inference. In IS, the samples are simulated …

The sampling problem is one of the most widely studied topics in computational chemistry.
While various methods exist for sampling along a set of reaction coordinates, many require …

We show how to speed up sequential Monte Carlo (SMC) for Bayesian inference in large
data problems by data subsampling. SMC sequentially updates a cloud of particles through …