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Abstract: Hidden Markov Models are one of the most popular and successful techniques used in statistical pattern recognition. However, they are not well understood on a fundamental level. For example, we do not knowhowtocharacterize the class of processes that can be well approximated by HMMs. This thesis tries to uncover the source of the intrinsic expressiveness of HMMs by studying when and whytwo models may represent the same stochastic process. Define two statistical models to be equivalent if they... (Update)
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BibTeX entry: (Update)
@techreport{ balasubramanian93equivalence,
author = "Vijay Balasubramanian",
title = "Equivalence and Reduction of Hidden Markov Models",
number = "AITR-1370",
pages = "111",
year = "1993",
url = "citeseer.ist.psu.edu/530769.html" }
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