The Oxford Handbook of Nonlinear Filtering (Hardcover)
Language: English
Published by Oxford University Press, Oxford, 2011
- Hardcover
- New
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
AbeBooks seller since June 29, 2022
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Hardcover. In many areas of human endeavour, the systems involved are not available for direct measurement. Instead, by combining mathematical models for a system's evolution with partial observations of its evolving state, we can make reasonable inferences about it. The increasing complexity of the modern world makes this analysis and synthesis of high-volume data an essential feature in many real-world problems.The celebrated Kalman-Bucy filter,designed for linear dynamical systems with linearly structured measurements, is the most famous Bayesian filter. Its generalizations to nonlinear systems and/or observations are collectively referred to asnonlinear filtering (NLF), an extension of the Bayesian framework to the estimation, prediction, and interpolation of nonlinear stochastic dynamics. NLF uses a stochastic model to make inferences about an evolving system and is a theoretically optimal algorithm.The breadth of its applications, firmly established and still emerging, is simply astounding. Early uses such as cryptography, tracking, and guidance were mostly of a military nature. Since then, the scope hasexploded. It includes the study of global climate, estimating the state of the economy, identifying tumours using non-invasive methods, and much more.The Oxford Handbook of NonlinearFiltering is the first comprehensive written resource for the subject. It contains classical and recent results and applications, with contributions from 58 authors. Collated into 10 parts, it covers the foundations of nonlinear filtering, connections to stochastic partial differential equations, stability and asymptotic analysis, estimation and control, approximation theory and numerical methods for solving the nonlinear filtering problem (including particle methods). It also contains a partdedicated to the application of nonlinear filtering to several problems in mathematical finance. A comprehensive, interdisciplinary resource for nonlinear (or stochastic) filtering, this Handbook explores the classical theory, the recent advances, and the application of nonlinear filtering to mathematical finance. With contributions from 58 leading experts, it will prove invaluable to anyone working in, or wishing to know more about, the area. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…
Seller Inventory # 9780199532902
- Title
- The Oxford Handbook of Nonlinear Filtering (Hardcover)
- Author
- Dan Crisan
- Publisher
- Oxford University Press, Oxford
- Publication year
- 2011
- Condition
- new
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 0199532907
- ISBN 13
- 9780199532902
The celebrated Kalman-Bucy filter, designed for linear dynamical systems with linearly structured measurements, is the most famous Bayesian filter. Its generalizations to nonlinear systems and/or observations are collectively referred to as nonlinear filtering (NLF), an extension of the Bayesian framework to the estimation, prediction, and interpolation of nonlinear stochastic dynamics. NLF uses a stochastic model to make inferences about an evolving system and is a theoretically optimal algorithm.
The breadth of its applications, firmly established and still emerging, is simply astounding. Early uses such as cryptography, tracking, and guidance were mostly of a military nature. Since then, the scope has exploded. It includes the study of global climate, estimating the state of the economy, identifying tumours using non-invasive methods, and much more.
The Oxford Handbook of Nonlinear Filtering is the first comprehensive written resource for the subject. It contains classical and recent results and applications, with contributions from 58 authors. Collated into 10 parts, it covers the foundations of nonlinear filtering, connections to stochastic partial differential equations, stability and asymptotic analysis, estimation and control, approximation theory and numerical methods for solving the nonlinear filtering problem (including particle methods). It also contains a part dedicated to the application of nonlinear filtering to several problems in mathematical finance.
"Synopsis" may belong to another edition of this title.
About the Author
Boris Rozovskii is Ford Foundation Professor at Brown University. His main interests are in stochastic partial differential equations (SPDEs) and their applications.
"About the title" may belong to another edition of this title.
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