MCMC methods for discrete source separation

Details MCMC methods for discrete source separation MCMC methods for discrete source separation

May 2001

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2001aipc..568..350s&link_type=abstract

BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 20th International Workshop. AIP Conference Proceedi

Computer Science

Information Theory

1

Data Analysis: Algorithms And Implementation, Data Management, Information Theory And Communication Theory

Scientific paper

Source separation consists in recovering signals mixed by an unknown transmission channel. Likelihood and information theory or higher order statistics can be used to perform the separation. This paper proposes a Bayesian approach to the problem of an instantaneous linear mixing, considering the source signals are discrete valued. The Bayesian inference enables to take in account jointly prior information and the information available on the observation signals. This approach implies complex calculations which can be achieved through Monte Carlo Markov Chain (MCMC) simulation methods. The separation method for binary inputs was exposed in and is now extended to PSK source signals. .

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