Bayesian framework applied to a source separation problem using a priori knowledge concerning ill conditioned mixtures issued from an Eddy current sensor response

Details Bayesian framework applied to a source separation problem using a priori knowledge concerning ill conditioned mixtures issued from an Eddy current sensor response Bayesian framework applied to a source separation problem using a priori knowledge concerning ill conditioned mixtures issued from an Eddy current sensor response

May 2001

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2001aipc..568..328h&link_type=abstract

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

Computer Science

Information Theory

Nondestructive Testing: Electromagnetic Testing, Eddy-Current Testing, Data Analysis: Algorithms And Implementation, Data Management, Information Theory And Communication Theory

Scientific paper

In this paper, we deal with the Bayesian framework applied to the source separation problem. Previous work showed that, the incorporation of additional information available from prior experiments or knowledge about the physics of the specific problem, is possible. Motivated by the ill conditioned nature of experimental signals issued from an eddy current sensor nonlinear response, we illustrate how one can incorporate prior information about the mixing matrix using Bayesian formalism. From this, we derive a modified Bell-Sejnowski learning rule. Results of its application to simulated and real world signals are provided. .

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