Measure Theory and Filtering: Introduction with Applications (Cambridge Series in Statistical and Probabilistic Mathematics) - Hardcover
Book 9 of 46: Cambridge Series in Statistical and Probabilistic Mathematics
Synopsis
Aimed primarily at those outside of the field of statistics, this book not only provides an accessible introduction to measure theory, stochastic calculus, and stochastic processes, with particular emphasis on martingales and Brownian motion, but develops into an excellent user's guide to filtering. Including exercises for students, it will be a complete resource for engineers, signal processing researchers, or anyone with an interest in practical implementation of filtering techniques, in particular, the Kalman filter. Three separate chapters concentrate on applications arising in finance, genetics, and population modelling.
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About the Author
Robert Elliott is the RBC Financial Group Professor of Finance at the University of Calgary, Canada.
Review
"It is well written and self-contained. I am convinced that it will raise a lot of interest and remain a reference for a long time to come." Alain Bensoussan, University of Texas at Dallas
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Measure Theory and Filtering: Introduction with Applications (Cambridge Series in Statistical and Probabilistic Mathematics)
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Measure Theory and Filtering: Introduction with Applications (Cambridge Series in Statistical and Probabilistic Mathematics)
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Measure Theory and Filtering: Introduction with Applications (Cambridge Series in Statistical and Probabilistic Mathematics)
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Measure Theory and Filtering: Introduction with Applications (Cambridge Series in Statistical and Probabilistic Mathematics)
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ISBN 13: 9780521838030
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Measure Theory and Filtering: Introduction and Applications (Hardcover)
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Hardcover. Condition: new. Hardcover. 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. Aimed primarily at those outside of the field of statistics, this book not only provides an accessible introduction to measure theory, stochastic calculus, and stochastic processes, with particular emphasis on martingales and Brownian motion, but develops into an excellent user's guide to filtering. Including exercises for students, it will be a complete resource for engineers, signal processing researchers, or anyone with an interest in practical implementation of filtering techniques, in particular, the Kalman filter. Three separate chapters concentrate on applications arising in finance, genetics, and population modelling. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9780521838030
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Measure Theory and Filtering: Introduction with Applications (Cambridge Series in Statistical and Probabilistic Mathematics)
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Measure Theory and Filtering: An Introduction With Applications
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MEASURE THEORY AND FLITERING INTRODUCTION WITH APPLICATIONS (HB 2004)
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Measure Theory and Filtering: Introduction and Applications
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Measure Theory and Filtering (Hardcover)
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ISBN 10: 0521838037
ISBN 13: 9780521838030
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Hardcover. Condition: new. Hardcover. 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. This book provides an accessible introduction to measure theory and stochastic calculus, and develops into an excellent users' guide to filtering. A complete resource for engineers, or anyone with an interest in implementation of filtering techniques. Three chapters concentrate on applications from finance, genetics and population modelling. Also includes exercises. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9780521838030
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