Lakhdar aggoun (45 risultati)

Hardcover. Condizione: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

hardcover. Condizione: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.

Hardcover. Condizione: Good. No Dust Jacket. 258 pages. Light discoloring and wear; a sound binding; good overall. Quantity Available: 1. Category: Mathematics; ISBN: 0521838037. ISBN/EAN: 9780521838030. Inventory No: 230383.

Condizione: New.

Hardcover. Condizione: Fine. Second Printing. No markings.

Condizione: New. In English.

hardcover. Condizione: Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority.

Condizione: New. pp. 270.

Pappband. Condizione: Gut. 1st ed. 1995. Corr. 3rd printing 2008. Oktav Applications of Mathematics. Stochastic Modelling and Applied Probability, 29. 382 S. Orig.-Pappband. Gutes Exemplar. Aufgrund der EPR-Regelung zur Zeit KEIN Versand in EU-Länder. Due to EPR, there is currently no delivery to EU-countries.…

hardcover. Condizione: New. In shrink wrap. Looks like an interesting title.

Condizione: New.

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, 10 tables 95 exercises. BIC Classification: PBK; PBT; TJK. Category: (P) Professional & Vocational. Dimension: 262 x 178 x 23. Weight in Grams: 664. . 2004. hardcover. . . . . …

Condizione: New. In English.

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. …

Condizione: New. pp. x + 258.

Hardcover. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

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. 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, 10 tables 95 exercises. BIC Classification: PBK; PBT; TJK. Category: (P) Professional & Vocational. Dimension: 262 x 178 x 23. Weight in Grams: 664. . 2004. hardcover. . . . . Books ship from the US and Ireland. …

Hardcover. Condizione: Brand New. 337 pages. 10.25x7.25x1.00 inches. In Stock.

Condizione: New.

Condizione: New.

Condizione: New. pp. 396.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Buch. 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. pp. 396 52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam.

Condizione: New. In English.

Condizione: As New. Unread book in perfect condition.

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - As more applications are found, interest in Hidden Markov Models continues to grow. Following comments and feedback from colleagues, students and other working with Hidden Markov Models the corrected 3rd printing of this volume contains clarifications, improvements and some new material, including results on smoothing for linear Gaussian dynamics. In Chapter 2 the derivation of the basic filters related to the Markov chain are each presented explicitly, rather than as special cases of one general filter. Furthermore, equations for smoothed estimates are given. The dynamics for the Kalman filter are derived as special cases of the authors' general results and new expressions for a Kalman smoother are given. The Chapters on the control of Hidden Markov Chains are expanded and clarified. The revised Chapter 4 includes state estimation for discrete time Markov processes and Chapter 12 has a new section on robust control.…