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.
Highlights
Modern signal processing (SP) methods, rely very heavily on probabilistic and statistical tools; for example, they use stochastic models to represent the data observation process and the prior knowledge available, and they obtain solutions by performing statistical inference
In writing this paper we have sought to provide an introduction to stochastic simulation and optimization methods in a tutorial format, but which raised some interesting topics for future research
We have addressed a variety of Markov chain Monte Carlo (MCMC) methods and discussed surrogate methods, such as variational Bayes, the Bethe approach, belief and expectation propagation, and approximate message passing
Summary
SP methods are 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. Emilie Chouzenoux and Jean-Christophe Pesquet are with the Laboratoire d’Informatique Gaspard Monge, Universite Paris-Est, Champs sur Marne, France (email: {emilie.chouzenoux,jean-christophe.pesquet}@univ-paris-est)
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Journal of Selected Topics in Signal Processing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
