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Advanced Computational Methods for Bayesian Signal Processing

The problem of estimating some variables of interest from noisy observations is ubiquitous in different fields, such as signal processing, finance, oceanography, video tracking and so on. Computational methods are often required in Bayesian inference and nonlinear signal processing to deal with intractable posterior densities. For instance, Sequential Importance Sampling (a.k.a. particle filters) and Markov Chain Monte Carlo (MCMC) methods, which have been popular approaches within the statistical community for a long time, have been widely used in signal processing and communications applications. Over the last years, several extensions and variants of these two families of methods have been proposed in order to improve their performance (e.g., for the estimation of fixed parameters or dealing with multi-modal target densities): population Monte Carlo (PMC) schemes, particle MCMC (PMCMC), adaptive Monte Carlo approaches (i.e., MCMC with adaptive proposal functions), multiple try Metropolis (MTM) strategies, parallel Monte Carlo chains, etc. Some of these methods have found their way into the signal processing literature, but there are still many recent advanced Monte Carlo methods, developed within the statistical community, that are not so widely known by signal processing practitioners and which may be very useful for signal processing applications. This special issue intends to bridge the gap between both communities by presenting a collection of papers that describe recent advances in Monte Carlo methods with signal processing applications in mind.

Edited by: François Desbouvries, David Luengo, Monica Bugallo, Victor Elvira, Fredrik Lindsted, Luca Martino, Jimmy Olsson, Yohan Petetin, Branco Ristic, Simo Sarkka and François Septier

  1. Research

    Particle rejuvenation of Rao-Blackwellized sequential Monte Carlo smoothers for conditionally linear and Gaussian models

    This paper focuses on sequential Monte Carlo approximations of smoothing distributions in conditionally linear and Gaussian state spaces. To reduce Monte Carlo variance of smoothers, it is typical in these mod...

    Ngoc Minh Nguyen, Sylvain Le Corff and Éric Moulines

    EURASIP Journal on Advances in Signal Processing 2017 2017:54

    Published on: 25 July 2017