The geometry of filtering

Filtering is the science of nding the law of a process given a partial observation of it. The main objects we study here are di usion processes. These are naturally associated with second-order linear di erential operators which are semi-elliptic and so introduce a possibly degenerate Riemannian str...

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Hlavní autoři: Elworthy, Kenneth David, 1940-, Le Jan, Yves, 1952- (Autor), Li, Xue-Mei, 1964-...., mathématicienne (Autor), Le Jan, Yves (Autor), Li, Xue-Mei (Autor)
Médium: Livre numérique
Jazyk:Anglais
Vydáno: Basel : Springer Basel [20..].
Cham : Springer Nature
Vydání:1st ed. 2010.
Edice:Frontiers in Mathematics
Témata:
On-line přístup:Accès sur la plateforme de l'éditeur
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Accès Université d'Orléans
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Poznámka: Description d'après consultation du 2014-05-07
Archives Springer e-books (Licence nationale)
Archives Springer e-books (Licence nationale)
Autres localisations: Voir dans le Sudoc
Edition sous un autre format:• The geometry of filtering, K. David Elworthy, Yves Le Jan, Xue-Mei Li, Basel, Birkhäuser, 2010, 1 vol. (XI-169 p.), Frontiers in mathematics, 978-3-0346-0175-7
• The geometry of filtering, K. David Elworthy, Yves Le Jan, Xue-Mei Li, Basel, Birkhäuser, 2010, 1 vol. (XI-169 p.), Frontiers in mathematics, 978-3-0346-0175-7
• The Geometry of Filtering, Texte imprimé, 9783034800822
• The geometry of filtering, K. David Elworthy, Yves Le Jan, Xue-Mei Li, Basel, Birkhäuser, 2010, 1 vol. (XI-169 p.), Frontiers in mathematics, 978-3-0346-0175-7
• The Geometry of Filtering, Texte imprimé, 9783034800822
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245 1 0 |a The geometry of filtering   |c K. David Elworthy, Yves Le Jan, Xue-Mei Li. 
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505 1 |a Diffusion Operators Decomposition of Diffusion Operators Equivariant Diffusions on Principal Bundles Projectible Diffusion Processes and Markovian Filtering Filtering with non-Markovian Observations The Commutation Property Example: Riemannian Submersions and Symmetric Spaces Example: Stochastic Flows Appendices 
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520 |a Filtering is the science of nding the law of a process given a partial observation of it. The main objects we study here are di usion processes. These are naturally associated with second-order linear di erential operators which are semi-elliptic and so introduce a possibly degenerate Riemannian structure on the state space. In fact, much of what we discuss is simply about two such operators intertwined by a smooth map, the \projection from the state space to the observations space", and does not involve any stochastic analysis. From the point of view of stochastic processes, our purpose is to present and to study the underlying geometric structure which allows us to perform the ltering in a Markovian framework with the resulting conditional law being that of a Markov process which is time inhomogeneous in general. This geometry is determined by the symbol of the operator on the state space which projects to a symbol on the observation space. The projectible symbol induces a (possibly non-linear and partially de ned) connection which lifts the observation process to the state space and gives a decomposition of the operator on the state space and of the noise. As is standard we can recover the classical ltering theory in which the observations are not usually Markovian by application of the Girsanov- Maruyama-Cameron-Martin Theorem. This structure we have is examined in relation to a number of geometrical topics 
650 |a Filtres (mathématiques) 
650 |a Processus gaussiens 
650 |a Markov, processus de 
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