Abstract
Hidden Markov models (HMMs) usually assume that the state transition matrices and the output models are time-invariant. Without this assumption, the parameters in a HMM may not be identifiable. In this paper, we propose a HMM with multiple observers such that its parameters are local identifiable without the time-invariant assumption. We show a sufficient condition for local identifiability of parameters in HMMS.
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© 2007 Springer-Verlag Berlin Heidelberg
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Chen, H., Geng, Z., Jia, J. (2007). Hidden Markov Models with Multiple Observers. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2007. Lecture Notes in Computer Science(), vol 4682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74205-0_47
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DOI: https://doi.org/10.1007/978-3-540-74205-0_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74201-2
Online ISBN: 978-3-540-74205-0
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