Dissertation/Thesis Abstract

New methods in theory & applications of nonlinear filtering
by Papanicolaou, Andrew, Ph.D., Brown University, 2010, 136; 3430207
Abstract (Summary)

Hidden Markov models are used in countless signal processing problems, and the associated nonlinear filtering algorithms are used to obtain posterior distributions for the hidden states. One reason why posterior distributions are so important is because they are used to compute estimates which are optimal given a history of observed data. However, it is often the case that implementation of these algorithms is near impossible because of the curse-of-dimensionality which results from testing every possible hypothesis. This thesis explores new applications of nonlinear filtering and addresses several issues in algorithm implementation.

Indexing (document details)
Advisor: Rozovsky, Boris
School: Brown University
School Location: United States -- Rhode Island
Source: DAI-B 71/11, Dissertation Abstracts International
Subjects: Applied Mathematics, Mathematics, Statistics
Keywords: Markov chain, Nonlinear filtering, Posterior distribution, Probabilistic recursion
Publication Number: 3430207
ISBN: 978-1-124-30222-5
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