A01=Daniel B. Rowe
advanced data analysis
Author_Daniel B. Rowe
Bayesian signal separation techniques
Bayesian Source Separation
Bayesian Source Separation models
Bayesian statistical decision theory
Category=PBT
computational neuroscience
conditional
Conjugate Prior Distributions
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Error Covariance Matrix
FMRI Participant
Generalized Beta Distribution
gibbs
Gibbs Sampling
ICM
inverted
Inverted Gamma
Inverted Gamma Distribution
Inverted Wishart Distributed
joint
Joint Posterior Distribution
Joint Prior Distribution
latent variable estimation
Marginal Posterior Distribution
matrix
Matrix Normal Distribution
Mixing Coefficients
multivariate Bayesian statistics
Multivariate Normal Random Variates
Multivariate Student T-distribution
normal
Observable Sources
Observation Error Covariance Matrix
posterior
Posterior Conditional
Posterior Conditional Distribution
Posterior Distribution
prior
Prior Distribution
probabilistic modeling
sampling
Scalar Normal Distribution
signal processing methods
statistical inference
wishart
Wishart Distribution
Product details
- ISBN 9781584883180
- Weight: 666g
- Dimensions: 156 x 234mm
- Publication Date: 25 Nov 2002
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Hardback
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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.
Daniel B. Rowe holds a joint appointment as an assistant professor of Biophysics and Biostatistics at the Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
