A Survey of Stochastic Simulation and Optimization Methods in Signal Processing

Marcelo Pereyra, Philip Schniter, Emilie Chouzenoux, Jean-Christophe Pesquet, Jean-Yves Tourneret, Alfred Hero, Steve McLaughlin

Research output: Contribution to journalArticle (Academic Journal)peer-review

89 Citations (Scopus)
361 Downloads (Pure)


Modern signal processing (SP) methods rely very heavily on probability and statistics to solve challenging SP problems. Expectations and demands are constantly rising, and 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 compu-tationally 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 paper presents a tutorial on stochas-tic simulation and optimization methods in signal and image processing and points to some interesting research problems. 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.
Original languageEnglish
Pages (from-to)224-241
Number of pages18
JournalIEEE Journal of Selected Topics in Signal Processing
Issue number2
Early online date2 Nov 2015
Publication statusPublished - 1 Mar 2016


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  • 8092 EPSRC D063485

    Nason, G. P.

    1/08/16 → …

    Project: Research

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