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Probability distance based compression of Hidden Markov Models

Please always quote using this URN:urn:nbn:de:0296-matheon-10894
  • Large-scale stochastic models are relevant in many different fields such as com- putational biology, finance, social sciences, communication and traffic networks. In order to both efficiently simulate and analyze such models and to understand the essential properties of the sys- tem, it is desirable to have model reduction techniques that much reduce the dimensionality of the model while at the same time preserving the system’s essential dynamical properties. In this paper, a general model reduction technique for the class of discrete space and time Hidden Markov Models is presented, thereby also including the more special class discrete Markov Chains. The method is illustrated on some model applications.

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Metadaten
Author:Hao Wu, Frank Noe
URN:urn:nbn:de:0296-matheon-10894
Referee:Christof Schütte
Document Type:Preprint, Research Center Matheon
Language:English
Date of first Publication:2012/02/27
Release Date:2012/02/27
Institute:Research Center Matheon
Freie Universität Berlin
MSC-Classification:62-XX STATISTICS / 62Mxx Inference from stochastic processes / 62M05 Markov processes: estimation
Preprint Number:933
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