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.
|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|
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