Application of Bayesian Non-negative Source Separation to Mixture Analysis in Spectroscopy | Zendy

Saïd Moussaoui | Zendy; David Brie | Zendy; Cédric Carteret | Zendy; Ali MohammadDjafari | Zendy

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Application of Bayesian Non-negative Source Separation to Mixture Analysis in Spectroscopy

Author(s) -

Saïd Moussaoui,

David Brie,

Cédric Carteret,

Ali MohammadDjafari

Publication year - 2004

Publication title -

aip conference proceedings

Language(s) - English

Resource type - Conference proceedings

SCImago Journal Rank - 0.177

H-Index - 75

eISSN - 1551-7616

pISSN - 0094-243X

DOI - 10.1063/1.1835218

Subject(s) - bayesian probability , source separation , computer science , blind signal separation , separation (statistics) , inference , bayesian inference , algorithm , artificial intelligence , data mining , pattern recognition (psychology) , machine learning , computer network , channel (broadcasting)

In this paper we present an application of Bayesian non‐negative source separation to the analysis of spectral mixtures obtained from the analysis of multicomponent substances. The processing aims are formalized as a non‐negative source separation problem. The proposed Bayesian inference for the analysis is introduced and the main steps of the estimation algorithm are outlined. Some results obtained with simulated and experimental data are presented.

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