Bayesian Signal Processing: Classical, Modern, and Particle Filtering Methods, 2e
James V. Candy, Lawrence Livermore National Laboratory
John Wiley & Sons, Inc., 2016
ISBN: 978-1-119-12545-7;
Language: English
Bayesian Signal Processing aims to give readers a unified Bayesian treatment: starting from the basics (Bayes' rule), to the more advanced (Monte Carlo sampling), and evolving to the next-generation model-based techniques (sequential Monte Carlo sampling). This next edition incorporates a new chapter on “sequential bayesian detection,” a new section on “ensemble Kalman filters”, as well as an expansion of case studies that detail Bayesian solutions for a variety of applications. These studies illustrate Bayesian approaches to real-world problems, incorporating detailed particle filter designs, adaptive particle filters, and sequential Bayesian detectors.
MATLAB notes at the end of each chapter help readers solve complex problems.
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