ABSTRACT

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

chapter Chapter 1|7 pages

Introduction

part I|1 pages

Fundamentals

chapter Chapter 2|19 pages

Statistical Distributions

chapter Chapter 3|9 pages

Introductory Bayesian Statistics

chapter Chapter 4|17 pages

Prior Distributions

chapter Chapter 5|7 pages

Hyperparameter Assessment

chapter Chapter 6|19 pages

Bayesian Estimation Methods

chapter Chapter 7|23 pages

Regression

part II|1 pages

Models

chapter Chapter 8|25 pages

Bayesian Regression

chapter Chapter 9|30 pages

Bayesian Factor Analysis

chapter Chapter 10|37 pages

Bayesian Source Separation

chapter Chapter 11|21 pages

Unobservable and Observable Source Separation

chapter Chapter 12|21 pages

FMRI Case Study

part III|1 pages

Generalizations

chapter Chapter 13|24 pages

Delayed Sources and Dynamic Coefficients

chapter Chapter 14|29 pages

Correlated Observation and Source Vectors

chapter Chapter 15|1 pages

Conclusion