CUED Publications database

Bayesian signal processing

Fitzgerald, WJ and Rayner, PJW (1999) Bayesian signal processing. IEE Colloquium (Digest). pp. 35-40. ISSN 0963-3308

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The application of Bayes' Theorem to signal processing provides a consistent framework for proceeding from prior knowledge to a posterior inference conditioned on both the prior knowledge and the observed signal data. The first part of the lecture will illustrate how the Bayesian methodology can be applied to a variety of signal processing problems. The second part of the lecture will introduce the concept of Markov Chain Monte-Carlo (MCMC) methods which is an effective approach to overcoming many of the analytical and computational problems inherent in statistical inference. Such techniques are at the centre of the rapidly developing area of Bayesian signal processing which, with the continual increase in available computational power, is likely to provide the underlying framework for most signal processing applications.

Item Type: Article
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:47
Last Modified: 07 Mar 2019 12:52