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Backwards Markovian Models for Second Order Stochastic Processes
Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
Stanford University, CA, USA.
1976 (engelsk)Inngår i: IEEE Transactions on Information Theory, ISSN 0018-9448, E-ISSN 1557-9654, Vol. 22, nr 4, s. 488-491Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

A state-space model of a second-order random process is a representation as a linear combination of a set of state-variables which obey first-order linear differential equations driven by an input process that is both white and uncorrelated with the initial values of the state-variables. Such a representation is often called a Markovian representation. There are applications in which it is useful to consider time running backwards and to obtain corresponding backwards Markovian representations. Except in one very special circumstance, these backwards representations cannot be obtained simply by just reversing the direction of time in a forwards Markovian representation. We show how this problem can be solved, give some examples, and also illustrate how the backwards model can be used to clarify certain least squares smoothing formulas.

sted, utgiver, år, opplag, sider
IEEE Information Theory Society , 1976. Vol. 22, nr 4, s. 488-491
Emneord [en]
Least-squares estimation, Markov processes, State estimation, Stochastic processes
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-100791DOI: 10.1109/TIT.1976.1055570OAI: oai:DiVA.org:liu-100791DiVA: diva2:663699
Tilgjengelig fra: 2013-11-12 Laget: 2013-11-12 Sist oppdatert: 2017-12-06

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