Back to Search Start Over

A Survey of Stochastic Simulation and Optimization Methods in Signal Processing

Authors :
Pereyra, Marcelo
Schniter, Philip
Chouzenoux, Emilie
Pesquet, Jean-Christophe
Tourneret, Jean-Yves
Hero, Alfred
McLaughlin, Steve
Publication Year :
2015

Abstract

Modern signal processing (SP) methods rely very heavily on probability and statistics to solve challenging SP problems. SP methods are now expected to deal with ever more complex models, requiring ever more sophisticated computational inference techniques. This has driven the development of statistical SP methods based on stochastic simulation and optimization. Stochastic simulation and optimization algorithms are computationally intensive tools for performing statistical inference in models that are analytically intractable and beyond the scope of deterministic inference methods. They have been recently successfully applied to many difficult problems involving complex statistical models and sophisticated (often Bayesian) statistical inference techniques. This survey paper offers an introduction to stochastic simulation and optimization methods in signal and image processing. The paper addresses a variety of high-dimensional Markov chain Monte Carlo (MCMC) methods as well as deterministic surrogate methods, such as variational Bayes, the Bethe approach, belief and expectation propagation and approximate message passing algorithms. It also discusses a range of optimization methods that have been adopted to solve stochastic problems, as well as stochastic methods for deterministic optimization. Subsequently, areas of overlap between simulation and optimization, in particular optimization-within-MCMC and MCMC-driven optimization are discussed.<br />Comment: To appear in the IEEE Journal of Selected Topics in Signal Processing special issue on Stochastic Simulation and Optimisation in Signal Processing, March 2016

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.1505.00273
Document Type :
Working Paper
Full Text :
https://doi.org/10.1109/JSTSP.2015.2496908