Statistical signal processing applications usually require the estimation of some parameters
of interest given a set of observed data. These estimates are typically obtained either by …

Bayesian methods and their implementations by means of sophisticated Monte Carlo
techniques have become very popular in signal processing over the last years. Importance …

This paper presents a novel algorithm, the particle-based, rapid incremental smoother
(PaRIS), for efficient online approximation of smoothed expectations of additive state …

Bagging (ie, bootstrap aggregating) involves combining an ensemble of bootstrap
estimators. We consider bagging for inference from noisy or incomplete measurements on a …

A pseudo-marginal sequential Monte Carlo online smoothing algorithm Page 1 Bernoulli 28(4),
2022, 2606–2633 https://doi.org/10.3150/21-BEJ1431 A pseudo-marginal sequential Monte …

This paper introduces a class of Monte Carlo algorithms which are based on the simulation
of a Markov process whose quasi-stationary distribution coincides with a distribution of …

We present a novel sequential Monte Carlo approach to online smoothing of additive
functionals in a very general class of path-space models. Hitherto, the solutions proposed in …

We demonstrate that the method of interleaved resampling in the context of parton showers
can tremendously improve the statistical convergence of weighted parton shower evolution …

Considerable effort has been recently devoted to the design of schemes for the parallel
implementation of sequential Monte Carlo (SMC) methods for dynamical systems, also …

The bootstrap particle filter (BPF) is the cornerstone of many algorithms used for solving
generally intractable inference problems with hidden Markov models. The long‐term stability …