Bayesian Signal Processing: Classical, Modern and Particle by James V. Candy

By James V. Candy

New Bayesian procedure is helping you resolve difficult difficulties in sign processing with easeSignal processing relies in this basic concept—the extraction of severe details from noisy, doubtful information. such a lot options depend upon underlying Gaussian assumptions for an answer, yet what occurs while those assumptions are misguided? Bayesian innovations stay clear of this drawback through supplying a very various process that may simply include non-Gaussian and nonlinear techniques besides all the ordinary equipment presently available.This textual content allows readers to completely take advantage of the numerous merits of the "Bayesian strategy" to model-based sign processing. It truly demonstrates the positive aspects of this robust method in comparison to the natural statistical tools present in different texts. Readers will realize how simply and successfully the Bayesian strategy, coupled with the hierarchy of physics-based versions constructed all through, may be utilized to sign processing difficulties that in the past appeared unsolvable.Bayesian sign Processing gains the newest new release of processors (particle filters) which were enabled via the arrival of high-speed/high-throughput pcs. The Bayesian method is uniformly constructed during this book's algorithms, examples, functions, and case reviews. all through this booklet, the emphasis is on nonlinear/non-Gaussian difficulties; besides the fact that, a few classical concepts (e.g. Kalman filters, unscented Kalman filters, Gaussian sums, grid-based filters, et al) are incorporated to let readers accustomed to these easy methods to draw parallels among the 2 approaches.Special good points include:Unified Bayesian remedy ranging from the fundamentals (Bayes's rule) to the extra complex (Monte Carlo sampling), evolving to the next-generation thoughts (sequential Monte Carlo sampling)Incorporates "classical" Kalman filtering for linear, linearized, and nonlinear platforms; "modern" unscented Kalman filters; and the "next-generation" Bayesian particle filtersExamples illustrate how idea could be utilized on to quite a few processing problemsCase stories display how the Bayesian method solves real-world difficulties in practiceMATLAB® notes on the finish of every bankruptcy aid readers remedy complicated difficulties utilizing available software program instructions and indicate software program applications availableProblem units attempt readers' wisdom and aid them positioned their new talents into practiceThe easy Bayesian procedure is emphasised all through this article with a view to permit the processor to reconsider the method of formulating and fixing sign processing difficulties from the Bayesian viewpoint. this article brings readers from the classical equipment of model-based sign processing to the subsequent new release of processors that may essentially dominate the way forward for sign processing for future years. With its many illustrations demonstrating the applicability of the Bayesian method of real-world difficulties in sign processing, this article is vital for all scholars, scientists, and engineers who examine and observe sign processing to their daily difficulties.

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Additional info for Bayesian Signal Processing: Classical, Modern and Particle Filtering Methods (Adaptive and Learning Systems for Signal Processing, Communications and Control Series)

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4 BATCH MINIMUM VARIANCE ESTIMATION M-step: λˆ m (i) = λˆ m (i − 1) Nd n=1 ⎛ ⎝ 33 ⎞ yn pmn Mp ˆ j=1 λj (i − 1)pjn ⎠ More details can be found in [6, 14, 16]. This completes our discussion of the EM approach to parameter estimation problems with incomplete data/parameters. Next we briefly describe one of the most popular approaches to the estimation problem—minimum variance (MV ) or equivalently minimum mean-squared error (MMSE) estimation. 4 BATCH MINIMUM VARIANCE ESTIMATION To complete this discussion evolving from the batch Bayesian perspective, we discuss the development of the minimum (error) variance (MV ) or equivalently minimum mean-squared error (MMSE) estimator.

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