Sequential Monte Carlo methods—also known as particle filters—offer approximate
solutions to filtering problems for nonlinear state-space systems. These filtering problems …

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

Automatic differentiation (AD), a technique for constructing new programs which compute
the derivative of an original program, has become ubiquitous throughout scientific …

Simultaneous localization and map** (SLAM) remains challenging for a number of
downstream applications, such as visual robot navigation, because of rapid turns …

Ensemble Kalman methods are widely used for state estimation in the geophysical sciences.
Their success stems from the fact that they take an underlying (possibly noisy) dynamical …

Abstract We propose Continual Repeated Annealed Flow Transport Monte Carlo (CRAFT), a
method that combines a sequential Monte Carlo (SMC) sampler (itself a generalization of …

Data assimilation is concerned with sequentially estimating a temporally evolving state. This
task, which arises in a wide range of scientific and engineering applications, is particularly …

We study the stability of entropically regularized optimal transport with respect to the
marginals. Lipschitz continuity of the value and Hölder continuity of the optimal coupling in …

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

We cast the resampling step in particle filters (PFs) as a variational inference problem,
resulting in a new class of resampling schemes: variational resampling. Variational …