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, and Steve McLaughlin

TL;DR
This survey reviews stochastic simulation and optimization techniques in signal processing, highlighting their role in tackling complex models and Bayesian inference through methods like MCMC, variational Bayes, and message passing.
Contribution
It provides a comprehensive overview of recent stochastic simulation and optimization methods applied to high-dimensional signal processing problems, including their integration and overlap.
Findings
Coverage of high-dimensional MCMC methods
Discussion of surrogate deterministic methods
Analysis of stochastic optimization techniques
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…
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