Multivariate Bayesian Statistics | Models for Source Separation and Signal Unmixing

Daniel B. Rowe

ISBN 10: 0367454661 / ISBN 13: 9780367454661

Published by Taylor & Francis, 2019

About this Item

Multivariate Bayesian Statistics | Models for Source Separation and Signal Unmixing | Daniel B. Rowe | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2019 | Taylor & Francis | EAN 9780367454661 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 131008334

Bibliographic Details

• Title: Multivariate Bayesian Statistics | Models for Source Separation and Signal Unmixing
• Publisher: Taylor & Francis
• Publication Date: 2019
• Language: English
• ISBN 10: 0367454661
• ISBN 13: 9780367454661
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 19W x 156H x 234D millimeters 19 millimeters width by 156 millimeters height by 234 millimeters depth
• Weight: 650 grams
• Seller catalogs:
  • Bücher

About this title

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 also allow inferences to be drawn from them. Multivariate Bayesian Statistics: Models for Source Separation and Signal Unmixing offers a thorough, self-contained treatment of the source separation problem. After an introduction to the problem using the "cocktail-party" analogy, Part I provides the statistical background needed for the Bayesian source separation model. Part II considers the instantaneous constant mixing models, where the observed vectors and unobserved sources are independent over time but allowed to be dependent within each vector. Part III details more general models in which sources can be delayed, mixing coefficients can change over time, and observation and source vectors can be correlated over time. For each model discussed, the author gives two distinct ways to estimate the parameters. Real-world source separation problems, encountered in disciplines from engineering and computer science to economics and image processing, are more difficult than they appear. This book furnishes the fundamental statistical material and up-to-date research results that enable readers to understand and apply Bayesian methods to help solve the many "cocktail party" problems they may confront in practice.

About the Author

Daniel B. Rowe holds a joint appointment as an assistant professor of Biophysics and Biostatistics at the Medical College of Wisconsin, Milwaukee, Wisconsin, USA.