Fundamentals of Stochastic Filtering
Language: English
Published by Springer New York, 2008
Series: Book 17 of 30 - Stochastic Modelling and Applied Probability
- Hardcover
- New
Condition: New
£ 126.34
Quantity: Over 20 available
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Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The authors are an authority in the stochastic filtering fieldAn assortment of Measure Theory, Probability Theory and Stochastic Analysis results are included in order to make this book as self contained as possibleExercises and solutions i.
Seller Inventory # 5911093
- Title
- Fundamentals of Stochastic Filtering
- Author
- Alan Bain|Dan Crisan
- Publisher
- Springer New York
- Publication year
- 2008
- Condition
- New
- Binding
- Gebunden
- Language
- English
- ISBN 10
- 0387768955
- ISBN 13
- 9780387768953
- Dimensions
- 2.5 x 24 x 16.5
- Series
- Book 17 of 30: Stochastic Modelling and Applied Probability
This book provides 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. Exercises and solutions are included.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
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