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Measure theory and filtering :introduction and applications /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	515/.42
書名/作者:	Measure theory and filtering : : introduction and applications // Lakhdar Aggoun, Robert J. Elliott.
其他題名:	Measure Theory & Filtering
作者:	Aggoun, Lakhdar,
其他作者:	Elliott, Robert J.
面頁冊數:	1 online resource (x, 258 pages) : : digital, PDF file(s).
附註:	Title from publisher's bibliographic system (viewed on 05 Oct 2015).
標題:	Measure theory.
標題:	Kalman filtering.
ISBN:	9780511755330 (ebook)
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1017/CBO9780511755330

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 ;36..

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:

495639
Measure theory.

LC Class. No.: QA312 / .A34 2004

Dewey Class. No.: 515/.42

Measure theory and filtering :introduction and applications /
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