Product Estimators for Hidden Markov Models
dc.contributor.advisor | Marcus, Steven | en_US |
dc.contributor.author | Ramezani, Vahid Reza | en_US |
dc.contributor.department | ISR | en_US |
dc.date.accessioned | 2007-05-23T10:11:00Z | |
dc.date.available | 2007-05-23T10:11:00Z | |
dc.date.issued | 2001 | en_US |
dc.description.abstract | In this thesis, risk-sensitive estimation for Hidden Markov Models isstudied from a dynamical systems point of view. We show that risk-sensitive estimators belong to a broaderclass of product estimators in which risk-sensitivity willbe shown to be related to certain scaling functions.The product structureand the scaling functions perspective give us new insights into the underlying mechanism of risk-sensitive estimation.For the first time, in a series of theorems and examples, we relate risk-sensitivity to the dynamics of the underlying process and exposerelations among the transition probabilities, risk-sensitivity andthe decision regions. We introduce the risk-sensitive Maximum A Posterior Probability (MAP) criterion for HMM's with discrete rangeobservation. This criterion is the discrete time finite dimensionalversion of the classic risk-sensitive estimation problem for linear/quadratic partial observation case. <p>The risk-sensitive filters take into account the"higher order" moments of the of the estimation error. In the context of risk-sensitive MAP for HMM's, we clarify and quantify the influence of risk-sensitivityon the behavior of the sample paths of the estimator; theproduct structure representationwill play an important role. | en_US |
dc.format.extent | 1396857 bytes | |
dc.format.mimetype | application/pdf | |
dc.identifier.uri | http://hdl.handle.net/1903/6215 | |
dc.language.iso | en_US | en_US |
dc.relation.ispartofseries | ISR; PhD 2001-7 | en_US |
dc.subject | Next-Generation Product Realization Systems | en_US |
dc.title | Product Estimators for Hidden Markov Models | en_US |
dc.type | Dissertation | en_US |
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