Fast, approximate maximum a Posteriori probability parameter estimation
Document Type
Article
Date of Original Version
12-1-1997
Abstract
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.
Publication Title, e.g., Journal
IEEE Signal Processing Letters
Volume
4
Issue
4
Citation/Publisher Attribution
Harrison, Brian F., Donald W. Tufts, and Richard J. Vaccaro. "Fast, approximate maximum a Posteriori probability parameter estimation." IEEE Signal Processing Letters 4, 4 (1997): 96-99. doi: 10.1109/97.566699.