Model selection by MCMC computation

Journal article

MCMC sampling is a methodology that is becoming increasingly important in statistical signal processing. It has been of particular importance to the Bayesian-based approaches to signal processing since it extends significantly the range of problems that they can address. MCMC techniques generate samples from desired distributions by embedding them as limiting distributions of Markov chains. There are many ways of categorizing MCMC methods, but the simplest one is to classify them in one of tw...

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Authors: Andrieu, C; Djuric, P; Doucet, A

• Publication status: Published
• Journal: SIGNAL PROCESSING
• Volume: 81
• Issue: 1
• Pages: 19-37
• Publication date: 2001-01-01
• DOI: 10.1016/S0165-1684(00)00188-2
• ISSN: 0165-1684
• Language: English
• Keywords: Markov chain Monte Carlo methods; reversible jump MCMC; Bayesian model selection