1st Edition
Multivariate Bayesian Statistics Models for Source Separation and Signal Unmixing
By Daniel B. Rowe
Copyright 2002
350 Pages
by
Chapman & Hall
350 Pages
19 B/W Illustrations
by
Chapman & Hall
352 Pages
by
Chapman & Hall
Also available as eBook on:
Of the two primary approaches to the classic source separation problem, only one does not impose potentially unreasonable model and likelihood constraints: the Bayesian statistical approach. Bayesian methods incorporate the available information regarding the model parameters and not only allow estimation of the sources and mixing coefficients, but also allow inferences to be drawn from them.... Read more
FUNDAMENTALS: Statistical Distributions. Introductory Bayesian Statistics. Prior Distribution. Hyperparameter Assessment. Bayesian Estimation Methods. MODELS: Introduction. Bayesian Regression. Bayesian Factor Analysis. Bayesian Source Separation. Unobservable and Observable Sources. fMRI Case Study. GENERALIZATIONS: Delayed sources and Dynamic Coefficients. Correlated Observation and Source Vectors. fMRI Case Study. APPENDICES: Activation Determination. fMRI Hyperparameter Assessment.
Biography
Daniel B. Rowe holds a joint appointment as an assistant professor of Biophysics and Biostatistics at the Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
