Measure Theory and Filtering: Introduction and Applications: 15 (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 15) - Hardcover
Book 9 of 45: Cambridge Series in Statistical and Probabilistic Mathematics
Synopsis
This book is a resource for non-statisticians implementing filtering methods, which covers applications in finance, genetics and population.
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About the Author
Lakhdar Aggoun is an Associate Professor in the Department of Mathematics and Statistics at Sultan Qabos University, Oman. Robert Elliott is the RBC Financial Group Professor of Finance at the University of Calgary, Canada.
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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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Measure Theory and Filtering: Introduction with Applications (Cambridge Series in Statistical and Probabilistic Mathematics)
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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: An Introduction With Applications
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Measure Theory and Filtering: Introduction and Applications
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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 our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9780521838030
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MEASURE THEORY AND FLITERING INTRODUCTION WITH APPLICATIONS (HB 2004)
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