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Measure theory and filtering : = introduction and applications /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Measure theory and filtering :/ Lakhdar Aggoun, Robert J. Elliott.
Reminder of title:	introduction and applications /
remainder title:	Measure Theory & Filtering
Author:	Aggoun, Lakhdar,
other author:	Elliott, Robert J.
Description:	1 online resource (x, 258 pages) :digital, PDF file(s). :
Notes:	Title from publisher's bibliographic system (viewed on 05 Oct 2015).
Subject:	Kalman filtering. -
Online resource:	https://doi.org/10.1017/CBO9780511755330
ISBN:	9780511755330 (ebook)

Measure theory and filtering : = introduction and applications /
Aggoun, Lakhdar,

Measure theory and filtering :introduction and applications /Measure Theory & FilteringLakhdar Aggoun, Robert J. Elliott. - 1 online resource (x, 258 pages) :digital, PDF file(s). - Cambridge series on statistical and probabilistic mathematics ;15. - Cambridge series on statistical and probabilistic mathematics ;32..

Title from publisher's bibliographic system (viewed on 05 Oct 2015).

This book was published in 2004. The estimation of noisily observed states from a sequence of data has traditionally incorporated ideas from Hilbert spaces and calculus-based probability theory. As conditional expectation is the key concept, the correct setting for filtering theory is that of a probability space. Graduate engineers, mathematicians and those working in quantitative finance wishing to use filtering techniques will find in the first half of this book an accessible introduction to measure theory, stochastic calculus, and stochastic processes, with particular emphasis on martingales and Brownian motion. Exercises are included. The book then provides an excellent users' guide to filtering: basic theory is followed by a thorough treatment of Kalman filtering, including recent results which extend the Kalman filter to provide parameter estimates. These ideas are then applied to problems arising in finance, genetics and population modelling in three separate chapters, making this a comprehensive resource for both practitioners and researchers.

ISBN: 9780511755330 (ebook)Subjects--Topical Terms:

579841
Kalman filtering.

LC Class. No.: QA312 / .A34 2004

Dewey Class. No.: 515/.42

Measure theory and filtering : = introduction and applications /
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500 $a Title from publisher's bibliographic system (viewed on 05 Oct 2015).
520 $a This book was published in 2004. The estimation of noisily observed states from a sequence of data has traditionally incorporated ideas from Hilbert spaces and calculus-based probability theory. As conditional expectation is the key concept, the correct setting for filtering theory is that of a probability space. Graduate engineers, mathematicians and those working in quantitative finance wishing to use filtering techniques will find in the first half of this book an accessible introduction to measure theory, stochastic calculus, and stochastic processes, with particular emphasis on martingales and Brownian motion. Exercises are included. The book then provides an excellent users' guide to filtering: basic theory is followed by a thorough treatment of Kalman filtering, including recent results which extend the Kalman filter to provide parameter estimates. These ideas are then applied to problems arising in finance, genetics and population modelling in three separate chapters, making this a comprehensive resource for both practitioners and researchers.
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