An introduction to stochastic filtering theory / Jie Xiong.

By:

Xiong, Jie

Material type: Text

TextSeries: Oxford graduate texts in mathematics ; 18. | Oxford mathematicsPublication details: Oxford, UK : Oxford University Press, 2008.Description: 1 online resource (xiii, 270 pages)Content type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

0191551392
9780199219704
0199219702
9780191551390
0191551392

Subject(s): Genre/Form:

Additional physical formats: Print version:: Introduction to stochastic filtering theory.DDC classification:

519.2/3 22 22

LOC classification:

QA274 .X56 2008eb

Online resources:

Click here to access online

Contents:

Contents; 1 Introduction; 2 Brownian motion and martingales; 3 Stochastic integrals and Itô's formula; 4 Stochastic differential equations; 5 Filtering model and Kallianpur-Striebel formula; 6 Uniqueness of the solution for Zakai's equation; 7 Uniqueness of the solution for the filtering equation; 8 Numerical methods; 9 Linear filtering; 10 Stability of non-linear filtering; 11 Singular filtering; Bibliography; List of Notations; Index.

Summary: Stochastic filtering theory is a field that has seen a rapid development in recent years and this book, aimed at graduates and researchers in applied mathematics, provides an accessible introduction covering recent developments. - ;Stochastic Filtering Theory uses probability tools to estimate unobservable stochastic processes that arise in many applied fields including communication, target-tracking, and mathematical finance. As a topic, Stochastic Filtering Theory has progressed rapidly in recent years. For example, the (branching) particle system representation of the optimal filter has bee.

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Electronic-Books	OPJGU Sonepat- Campus	E-Books EBSCO			Available

Includes bibliographical references and index.

Print version record.

Stochastic filtering theory is a field that has seen a rapid development in recent years and this book, aimed at graduates and researchers in applied mathematics, provides an accessible introduction covering recent developments. - ;Stochastic Filtering Theory uses probability tools to estimate unobservable stochastic processes that arise in many applied fields including communication, target-tracking, and mathematical finance. As a topic, Stochastic Filtering Theory has progressed rapidly in recent years. For example, the (branching) particle system representation of the optimal filter has bee.

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