Measure Theory and Filtering - Introduction and Applications (Paperback)

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.

General

Imprint:	Cambridge UniversityPress
Country of origin:	United Kingdom
Series:	Cambridge Series in Statistical and Probabilistic Mathematics
Release date:	September 2012
First published:	May 2012
Authors:	Lakhdar Aggoun • Robert J Elliott
Dimensions:	244 x 170 x 14mm (L x W x T)
Format:	Paperback - Trade
Pages:	270
ISBN-13:	978-1-107-41071-8
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Professional & Technical > Electronics & communications engineering > Communications engineering / telecommunications > General
LSN:	1-107-41071-1
Barcode:	9781107410718

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