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Bayesian Source Separation with Jointly Distributed Mean and Mixing Coefficients vie MCMC and ICM

Rowe, Daniel B. (2001) Bayesian Source Separation with Jointly Distributed Mean and Mixing Coefficients vie MCMC and ICM. Social Science Working Paper, 1119. California Institute of Technology , Pasadena, CA. (Unpublished)

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Recent source separation work has described a model which assumes a nonzero overall mean and incorporates prior knowledge regarding it. This is significant because source separation models that have previously been presented have assumed that the overall mean is zero. However, this work specified that the prior distribution which quantifies available prior knowledge regarding the overall mean be independent of the mixing coefficient matrix. The current paper generalizes this work by quantifying available prior information regarding the overall mean and mixing matrix with the use of joint prior distributions. This prior knowledge in the prior distributions is incorporated into the inferences along with the current data. Conjugate normal and generalized conjugate normal distributions are used. Algorithms for estimating the parameters of the model from the joint posterior distribution using both Gibbs sampling a Markov chain Monte Carlo method and the iterated conditional modes algorithm a deterministic optimization technique for marginal mean and maximum a posterior estimates respectively.

Item Type:Report or Paper (Working Paper)
Group:Social Science Working Papers
Series Name:Social Science Working Paper
Issue or Number:1119
Record Number:CaltechAUTHORS:20170807-133255278
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:79849
Deposited By: Jacquelyn Bussone
Deposited On:07 Aug 2017 22:08
Last Modified:03 Oct 2019 18:24

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