Approximation of stationary processes by hidden Markov models

DOI10.1007/S00498-010-0050-7zbMath1248.93151arXivmath/0606591OpenAlexW2115795564WikidataQ63196627 ScholiaQ63196627MaRDI QIDQ661013

Lorenzo Finesso, Angela Grassi, Peter Spreij

Publication date: 6 February 2012

Published in: MCSS. Mathematics of Control, Signals, and Systems (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/math/0606591

zbMATH Keywords

approximationHankel matrixKullback-Leibler divergencehidden Markov model (HMM)divergence ratenonnegative matrix factorization (NMF)

Mathematics Subject Classification ID

Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20) Stochastic systems in control theory (general) (93E03)

Related Items (2)

Equivalence of state representations for hidden Markov models ⋮ Structured nonnegative matrix factorization with applications to hidden Markov realization and clustering

Cites Work

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• Learning the parts of objects by non-negative matrix factorization
• On Stochastic Processes Derived From Markov Chains
• Statistical Inference for Probabilistic Functions of Finite State Markov Chains

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