Fast, approximate maximum a posteriori probability parameter estimation

In many estimation problems, the set of unknown parameters can be divided into a subset of desired parameters and a subset of nuisance parameters. Using a maximum a posteriori (MAP) approach to parameter estimation, these nuisance parameters are integrated out in the estimation process. This can res...

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Bibliographic Details
Published in:IEEE signal processing letters 1997-04, Vol.4 (4), p.96-99
Main Authors: Harrison, B.F., Tufts, D.W., Vaccaro, R.J.
Format: Article
Language:English
Subjects:
Acoustic noise
Array signal processing
Bayesian methods
Data models
Distributed computing
Monte Carlo methods
Parameter estimation
Probability density function
Probability distribution
Random variables
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Summary:In many estimation problems, the set of unknown parameters can be divided into a subset of desired parameters and a subset of nuisance parameters. Using a maximum a posteriori (MAP) approach to parameter estimation, these nuisance parameters are integrated out in the estimation process. This can result in an extremely computationally intensive estimator. This letter proposes a method by which computationally intensive integration over the nuisance parameters required in Bayesian estimation can be avoided under certain conditions. The proposed method is an approximate MAP estimator, which is much more computationally efficient than direct, or even Monte Carlo, integration of the joint posteriori distribution of the desired and nuisance parameters. As an example, we apply the fast algorithm to matched-field source localization in an uncertain environment.
ISSN:1070-9908
1558-2361
DOI:10.1109/97.566699