Bayesian signal processing: classical, modern, and particle filtering methods
Bayesian-based signal processing is expected to dominate the future of model-based signal processing for years to come. This book develops the 'Bayesian approach' to statistical signal processing for a variety of useful model sets with an emphasis on nonlinear/non-Gaussian problems, as well as classical techniques.
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- Full title :Bayesian signal processing : classical, modern, and particle filtering methods / James V. Candy.
Summary
Bayesian-based signal processing is expected to dominate the future of model-based signal processing for years to come. This book develops the 'Bayesian approach' to statistical signal processing for a variety of useful model sets with an emphasis on nonlinear/non-Gaussian problems, as well as classical techniques.
Read More ↓Edition Statements
- Publication Date:©2016
- Edition:Second edition
Publication Statements
- Publisher(s):John Wiley & Sons, Inc
- Place of Publication:Hoboken, New Jersey
- Language:eng
- Publication Date:©2016
Notes
Includes index.
The Physical Object
- Description:1 online resource
Edition Identifiers
- ISBN:9781119125471, 1119125472, 9781119125495, 1119125499, 9781523114887, 1523114886, 1119125456, 9781119125457, 9781119125488, 1119125480
- DOI:10.1002/9781119125495
- oclc:ocn952108134
- LC Classification:TK5102.9
- Dewey Decimal:621.382/201519542
Table of Contents
Bayesian Signal Processing -- Contents -- Preface to Second Edition -- References -- Preface to First Edition -- References -- Acknowledgments -- List of Abbreviations -- 1 Introduction -- 1.1 Introduction -- 1.2 Bayesian Signal Processing -- 1.3 Simulation-Based Approach to Bayesian Processing -- 1.3.1 Bayesian Particle Filter -- 1.4 Bayesian Model-Based Signal Processing -- 1.5 Notation and Terminology -- References -- 2 Bayesian Estimation -- 2.1 Introduction -- 2.2 Batch Bayesian Estimation -- 2.3 Batch Maximum Likelihood Estimation -- 2.3.1 Expectation-Maximization Approach to Maximum Likelihood -- 2.3.2 EM for Exponential Family of Distributions -- 2.4 Batch Minimum Variance Estimation -- 2.5 Sequential Bayesian Estimation -- 2.5.1 Joint Posterior Estimation -- 2.5.2 Filtering Posterior Estimation -- 2.5.3 Likelihood Estimation -- 2.6 Summary -- References -- 3 Simulation-Based Bayesian Methods -- 3.1 Introduction -- 3.2 Probability Density Function Estimation -- 3.3 Sampling Theory -- 3.3.1 Uniform Sampling Method -- 3.3.2 Rejection Sampling Method -- 3.4 Monte Carlo Approach -- 3.4.1 Markov Chains -- 3.4.2 Metropolis-Hastings Sampling -- 3.4.3 Random Walk Metropolis-Hastin…
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