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 …

A particle filtering (PF) is a sequential Bayesian filtering method suitable for non-linear non-
Gaussian systems, which is widely used to estimate the states of parameters of interest that …

A fundamental problem in signal processing is the estimation of unknown parameters or
functions from noisy observations. Important examples include localization of objects in …

Abstract The Effective Sample Size (ESS) is an important measure of efficiency of Monte
Carlo methods such as Markov Chain Monte Carlo (MCMC) and Importance Sampling (IS) …

Importance sampling (IS) methods are broadly used to approximate posterior distributions or
their moments. In the standard IS approach, samples are drawn from a single proposal …

Importance sampling (IS) is a Monte Carlo technique for the approximation of intractable
distributions and integrals with respect to them. The origin of IS dates from the early 1950s …

Variability indices are a key measure of interest across diverse fields, in and outside
psychology. A crucial problem for any research relying on variability measures however is …

The effective sample size (ESS) is widely used in sample‐based simulation methods for
assessing the quality of a Monte Carlo approximation of a given distribution and of related …

Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in
statistics, signal processing and machine learning. A well-known class of MC methods are …

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 …