Isbn: 9781107410718 - measure theory and filtering: introduction and applications (10 risultati)

Condizione: New.

Condizione: New. In.

Condizione: New. pp. 270.

Condizione: New. This book is a resource for non-statisticians implementing filtering methods, which covers applications in finance, genetics and population. Series: Cambridge Series in Statistical and Probabilistic Mathematics. Num Pages: 270 pages, Illustrations. BIC Classification: PBT; TJK. Category: (P) Professional & Vocational. Dimension: 243 x 168 x 15. Weight in Grams: 450. . 2012. Paperback. . . . . Books ship from the US and Ireland. …

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.…

Condizione: New. This book is a resource for non-statisticians implementing filtering methods, which covers applications in finance, genetics and population. Series: Cambridge Series in Statistical and Probabilistic Mathematics. Num Pages: 270 pages, Illustrations. BIC Classification: PBT; TJK. Category: (P) Professional & Vocational. Dimension: 243 x 168 x 15. Weight in Grams: 450. . 2012. Paperback. . . . . …

Condizione: New. Print on Demand pp. 270 67:B&W 6.69 x 9.61 in or 244 x 170 mm (Pinched Crown) Perfect Bound on White w/Gloss Lam.

Condizione: New. PRINT ON DEMAND pp. 270.

Paperback. Condizione: new. 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. 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 UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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. Thre.…