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- W2087284982 endingPage "1569" @default.
- W2087284982 startingPage "1518" @default.
- W2087284982 abstract "An overview of statistical and information-theoretic aspects of hidden Markov processes (HMPs) is presented. An HMP is a discrete-time finite-state homogeneous Markov chain observed through a discrete-time memoryless invariant channel. In recent years, the work of Baum and Petrie (1966) on finite-state finite-alphabet HMPs was expanded to HMPs with finite as well as continuous state spaces and a general alphabet. In particular, statistical properties and ergodic theorems for relative entropy densities of HMPs were developed. Consistency and asymptotic normality of the maximum-likelihood (ML) parameter estimator were proved under some mild conditions. Similar results were established for switching autoregressive processes. These processes generalize HMPs. New algorithms were developed for estimating the state, parameter, and order of an HMP, for universal coding and classification of HMPs, and for universal decoding of hidden Markov channels. These and other related topics are reviewed." @default.
- W2087284982 created "2016-06-24" @default.
- W2087284982 creator A5028774270 @default.
- W2087284982 creator A5077675980 @default.
- W2087284982 date "2002-06-01" @default.
- W2087284982 modified "2023-10-09" @default.
- W2087284982 title "Hidden Markov processes" @default.
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