Chapter 4. Bayesian Inference

  1. Saeed V. Vaseghi Professor

Published Online: 18 MAR 2009

DOI: 10.1002/9780470740156.ch4

Advanced Digital Signal Processing and Noise Reduction, Fourth Edition

Advanced Digital Signal Processing and Noise Reduction, Fourth Edition

How to Cite

Vaseghi, S. V. (2008) Bayesian Inference, in Advanced Digital Signal Processing and Noise Reduction, Fourth Edition, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470740156.ch4

Author Information

  1. Department of Electronics & Computer Engineering, Brunel University, London, UK

Publication History

  1. Published Online: 18 MAR 2009
  2. Published Print: 6 FEB 2008

ISBN Information

Print ISBN: 9780470754061

Online ISBN: 9780470740156

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Keywords:

  • Bayesian inference and human reasoning process;
  • estimate-maximise (EM) method;
  • estimation theory and ‘the best’ estimate determination;
  • Bayesian risk;
  • Bayesian estimation;
  • Expectation-Maximisation (EM) method;
  • Cramer–Rao Bound on minimum estimator variance;
  • Cramer–Rao inequality;
  • Gaussian Mixture Models (GMMs) design

Summary

This chapter contains sections titled:

  • Bayesian Estimation Theory: Basic Definitions

  • Bayesian Estimation

  • Expectation-Maximisation (EM) Method

  • Cramer–Rao Bound on the Minimum Estimator Variance

  • Design of Gaussian Mixture Models (GMMs)

  • Bayesian Classification

  • Modelling the Space of a Random Process

  • Summary

  • Bibliography