NDLI: Recursive filters for partially observable finite Markov chains

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Recursive filters for partially observable finite Markov chains

Content Provider	Scilit
Author	Ledoux, James
Copyright Year	2005
Description	In this note, we consider discrete-time finite Markov chains and assume that they are only partly observed. We obtain finite-dimensional normalized filters for basic statistics associated with such processes. Recursive equations for these filters are derived by means of simple computations involving conditional expectations. An application to the estimation of parameters of the so-called discrete-time batch Markovian arrival process is outlined.
Related Links	https://www.cambridge.org/core/services/aop-cambridge-core/content/view/8DE626E2174E993EB23B14F789DB9179/S002190020000070Xa.pdf/div-class-title-recursive-filters-for-partially-observable-finite-markov-chains-div.pdf
Ending Page	697
Page Count	14
Starting Page	684
ISSN	00219002
e-ISSN	14756072
DOI	10.1017/s002190020000070x
Journal	Journal of applied probability
Issue Number	03
Volume Number	42
Language	English
Publisher	Cambridge University Press (CUP)
Publisher Date	2005-09-01
Access Restriction	Open
Subject Keyword	Journal of applied probability Applied Mathematics inhomogeneous Markov Chain Hidden Markov Chain
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty

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