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

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

It is generally difficult to know whether the parameters in nonlinear econometric models are
point‐identified. We provide computationally attractive procedures to construct confidence …

Model comparison for the purposes of selection, averaging, and validation is a problem
found throughout statistics. Within the Bayesian paradigm, these problems all require the …

Sequential Monte Carlo methods, also known as particle methods, are a popular set of
techniques for approximating high-dimensional probability distributions and their …

We consider the inverse problem of estimating the initial condition of a partial differential
equation, which is observed only through noisy measurements at discrete time intervals. In …

Consider a reference Markov process with initial distribution $\pi_ {0} $ and transition
kernels $\{M_ {t}\} _ {t\in [1: T]} $, for some $ T\in\mathbb {N} $. Assume that you are given …

This paper introduces SMCP3, a method for automatically implementing custom sequential
Monte Carlo samplers for inference in probabilistic programs. Unlike particle filters and …

This article reviews the application of some advanced Monte Carlo techniques in the context
of multilevel Monte Carlo (MLMC). MLMC is a strategy employed to compute expectations …

The identification of parameters in mathematical models using noisy observations is a
common task in uncertainty quantification. We employ the framework of Bayesian inversion …