Fundamentals of stochastic filtering
The objective of stochastic filtering is to determine the best estimate for the state of a stochastic dynamical system from partial observations. The solution of this problem in the linear case is the well known Kalman-Bucy filter which has found widespread practical application. The purpose of this...
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| Principais autores: | , |
|---|---|
| Formato: | Livre numérique |
| Idioma: | Anglais |
| Publicado em: |
New York, NY :
Springer New York : Springer e-books : Imprint: Springer : Springer e-books
[20..].
Cham : Springer Nature |
| coleção: | Stochastic modelling and applied probability
60 |
| Assuntos: | |
| Acesso em linha: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Description d'après consultation du 29 septembre 2011 Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Fundamentals of stochastic filtering, Alan Bain, Dan Crisan, 2009, New York, Springer, 1 vol. (XIII-390 p.), Stochastic modelling and applied probability, 978-1-4419-2642-5 |
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| 100 | 1 | |a Bain, Alan, |d 19..- | |
| 245 | 1 | 0 | |a Fundamentals of stochastic filtering |c by Alan Bain, Dan Crisan ; edited by B. Rozovskii. |
| 260 | |a New York, NY : |b Springer New York : |b Springer e-books : |b Imprint: Springer : |b Springer e-books. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 490 | 0 | |a Stochastic modelling and applied probability |v 60 |x 0172-4568 | |
| 500 | |a Description d'après consultation du 29 septembre 2011 | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 504 | |a Bibliogr. Index | ||
| 505 | 1 | |a Introduction The Stochastic Process The Filtering Equations Uniqueness of the Solution to the Zakai and the Kushner-Stratonovitch Equations Other results Finite Dimensional Filters The Density of the Conditional Distribution of the Signal Numerical Methods for Solving the Filtering Problem A Continuous Time Particle Filter Particle Filters in Discrete Time Measure Theory Stochastic Analysis References | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a The objective of stochastic filtering is to determine the best estimate for the state of a stochastic dynamical system from partial observations. The solution of this problem in the linear case is the well known Kalman-Bucy filter which has found widespread practical application. The purpose of this book is to provide a rigorous mathematical treatment of the non-linear stochastic filtering problem using modern methods. Particular emphasis is placed on the theoretical analysis of numerical methods for the solution of the filtering problem via particle methods. The book should provide sufficient background to enable study of the recent literature. While no prior knowledge of stochastic filtering is required, readers are assumed to be familiar with measure theory, probability theory and the basics of stochastic processes. Most of the technical results that are required are stated and proved in the appendices. The book is intended as a reference for graduate students and researchers interested in the field. It is also suitable for use as a text for a graduate level course on stochastic filtering. Suitable exercises and solutions are included | ||
| 650 | |a Processus stochastiques | ||
| 650 | |a Filtres (mathématiques) | ||
| 700 | 1 | |a Cri—san, Dan, |d 19..- |4 aut | |
| 776 | 0 | |0 133636798 |t Fundamentals of stochastic filtering |f Alan Bain, Dan Crisan |d 2009 |c New York |n Springer |p 1 vol. (XIII-390 p.) |s Stochastic modelling and applied probability |z 978-1-4419-2642-5 | |
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