Multivariate Bayesian Statistics: Models for Source Separation and Signal Unmixing

✍ Scribed by Daniel B. Rowe (Author)

• Publisher: Chapman and Hall/CRC
• Year: 2002
• Leaves: 350
• Edition: 1
• Category: Library

✦ Synopsis

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

✦ Table of Contents

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.

✦ Subjects

Engineering & Technology;Electrical & Electronic Engineering;Digital Signal Processing;Mathematics & Statistics for Engineers;Mathematics & Statistics;Statistics & Probability;Statistics;Statistical Theory & Methods