EURASIP Journal on Advances in Signal Processing welcomes submissions to the new special issue on '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.
Throughout the years, the problems addressed in the field of statistical signal processing have become increasingly challenging. On the one hand, the probabilistic models have become more complex in an attempt to better capture the specific characteristics of the different applications. On the other hand, despite the availability of parallelized computing architectures and of always cheaper data storage, increasing flows of high dimensional data are well known to remain a major obstacle which impacts the applicability of available solutions. Altogether, these two facts imply that analytical solutions are impossible to compute in most practical applications, and thus efficient computational methods are called for. Unfortunately, many advanced computational methods, which have been recently developed within the field of Bayesian inference, are still not widely applied by signal processing practitioners. The main goal of this special issue is to introduce novel approaches from the statistical community to a wider signal processing audience. The focus will be both on the theoretical/methodological aspects (introducing recently developed algorithms) and their applications, especially within the field of Bayesian signal processing.
Potential topics include, but are not limited to:
- Sequential importance sampling (a.k.a. particle filters)
- SMC theory (convergence property, numerical stability)
- Sequential (or not) MC within SMC
- SMC for multi-target and/or multi-sensor filtering
- SMC based on evolutionary strategies
- Smoothing problem, path space SMC
- Quasi Monte Carlo (QMC) and Sequential Quasi Monte Carlo (SQMC) methods
- Particle MCMC (PMCMC) and Marginal MCMC
- Adaptive Monte Carlo approaches (MCMC with adaptive proposal functions, SMC samplers, Adaptive importance sampling (AIS), population Monte Carlo (PMC) schemes...)
- Data assimilation algorithms, Mixed SMC/Ensemble Kalman filter techniques
- Multiple try Metropolis (MTM) strategies
- Interacting Parallel Monte Carlo chains and island particle filters
- Computational methods for models with intractable likelihood
- Approximate Bayesian computation (ABC)
- Bayesian signal processing applications: localization and tracking, SLAM algorithms, model selection, parameter estimation in state space models, big data, etc.
Before submitting your manuscript, please ensure you have carefully read the Instructions for Authors for EURASIP Journal on Advances in Signal Processing. The complete manuscript should be submitted through the EURASIP Journal on Advances in Signal Processing submission system. To ensure that you submit to the correct special issue please select the appropriate section in the drop-down menu upon submission. In addition, indicate within your cover letter that you wish your manuscript to be considered as part of the special issue on 'Advanced Computational Methods for Bayesian Signal Processing'. All submissions will undergo rigorous peer review and accepted articles will be published within the journal as a collection.
Deadline for submissions: 1 March 2017
Lead guest editors:
François Desbouvries, Paris Saclay University, France
David Luengo, Universidad Politecnica de Madrid, Spain
Monica Bugallo, Stonybrook University, USA
Victor Elvira, Universidad Carlos III de Madrid, Spain
Fredrik Lindsten, Uppsala University, Sweden
Luca Martino, Universidade de Sao Paulo, Brazil
Jimmy Olsson, KTH Royal Institute of Technology, Sweden
Yohan Petetin, Paris Saclay University, France
Branco Ristic, RMIT University, Australia
Simo Sarkka, Aalto University, Finland
François Septier, Institut Mines-Télécom, France
Submissions will also benefit from the usual benefits of open access publication:
- Rapid publication: Online submission, electronic peer review and production make the process of publishing your article simple and efficient
- High visibility and international readership in your field: Open access publication ensures high visibility and maximum exposure for your work - anyone with online access can read your article
- No space constraints: Publishing online means unlimited space for figures, extensive data and video footage
- Authors retain copyright, licensing the article under a Creative Commons license: articles can be freely redistributed and reused as long as the article is correctly attributed
For editorial enquiries please contact firstname.lastname@example.org
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