The importance of Bayesian signal processing methods have grown over the past decade. A wealth of Bayesian tools are available for solving highly complex inference problems, including particle filters, Markov chain Monte Carlo, and variational Bayes. These methods can be utilized to solve some of the area's major challenges, from state and parameter estimation to decision/control. This book provides full coverage of the background material, including models, inference methods and case studies/examples in an accessible but not overly mathematical style.
Introduction. Fundamentals. Models. Deterministic inference methods. Monte Carlo methods. Sequential methods and particle filters. Emerging/advanced areas. Conclusions.
Simon John Godsill, PH.D., is a professor of statistical signal processing in the Engineering Department at the University of Cambridge, UK. Pete Bunch is a Ph.D. student at the University of Cambridge.
