Published by Cambridge University, 2004
ISBN 10: 0521838037 ISBN 13: 9780521838030
Language: English
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Published by Cambridge University Press, 2004
ISBN 10: 0521838037 ISBN 13: 9780521838030
Language: English
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Add to basketCondition: Used: Good. Occasion - Bon Etat - Measure theory and filtering. Introduction and applications (2012) - Grand Format.
Published by Cambridge University Press, 2012
ISBN 10: 1107410711 ISBN 13: 9781107410718
Language: English
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Published by Cambridge University Press, 2012
ISBN 10: 1107410711 ISBN 13: 9781107410718
Language: English
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Published by Cambridge University Press, 2012
ISBN 10: 1107410711 ISBN 13: 9781107410718
Language: English
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Published by Cambridge University Press, 2012
ISBN 10: 1107410711 ISBN 13: 9781107410718
Language: English
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Published by Cambridge University Press, 2004
ISBN 10: 0521838037 ISBN 13: 9780521838030
Language: English
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Published by Cambridge University Press, 2004
ISBN 10: 0521838037 ISBN 13: 9780521838030
Language: English
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Published by Cambridge University Press, 2004
ISBN 10: 0521838037 ISBN 13: 9780521838030
Language: English
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Published by Cambridge University Press, 2004
ISBN 10: 0521838037 ISBN 13: 9780521838030
Language: English
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Published by Cambridge University Press, Cambridge, 2004
ISBN 10: 0521838037 ISBN 13: 9780521838030
Language: English
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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.
Published by Cambridge University Press, 2004
ISBN 10: 0521838037 ISBN 13: 9780521838030
Language: English
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Published by Cambridge University Press, Cambridge, 2004
ISBN 10: 0521838037 ISBN 13: 9780521838030
Language: English
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Add to basketHardcover. 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 UK warehouse or from our Australian or US warehouses, depending on stock availability.
Published by Cambridge University Press, 2004
ISBN 10: 0521838037 ISBN 13: 9780521838030
Language: English
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Published by Cambridge University Press, 2012
ISBN 10: 1107410711 ISBN 13: 9781107410718
Language: English
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Add to basketTaschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is a resource for non-statisticians implementing filtering methods, which covers applications in finance, genetics and population.
Published by Cambridge University Press, Cambridge, 2004
ISBN 10: 0521838037 ISBN 13: 9780521838030
Language: English
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Add to basketHardcover. 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.
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Add to basketHardcover. Condition: Brand New. 337 pages. 10.25x7.25x1.00 inches. In Stock.
Published by Cambridge University Press, 2012
ISBN 10: 1107410711 ISBN 13: 9781107410718
Language: English
Seller: Revaluation Books, Exeter, United Kingdom
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Add to basketPaperback. Condition: Brand New. 1st edition. 268 pages. 9.75x6.75x0.75 inches. In Stock. This item is printed on demand.
Published by Cambridge University Press, 2004
ISBN 10: 0521838037 ISBN 13: 9780521838030
Language: English
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Seller: Revaluation Books, Exeter, United Kingdom
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Add to basketHardcover. Condition: Brand New. 337 pages. 10.25x7.25x1.00 inches. In Stock. This item is printed on demand.