Filtering and prediction is about observing moving objects when the observations are corrupted by random errors. This title includes chapters that deal with discrete probability spaces, random variables, conditioning, Markov chains, and filtering of discrete Markov chains. It also discusses filtering and prediction of stationary sequences. Series: Student Mathematical Library. Num Pages: 252 pages. BIC Classification: PBT. Category: (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 216 x 138 x 14. Weight in Grams: 314. . 2007. Paperback. . . . . Books ship from the US and Ireland.

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Bibliographic Details

Title: Filtering and Prediction: A Primer (Student Mathematical Library)
Publisher: American Mathematical Society
Publication Date: 2007
Language: English
ISBN 10: 0821843338
ISBN 13: 9780821843338
Binding: Soft cover
Condition: New

About this title

Synopsis

The authors stick to the basics in describing the process of observing moving objects when the observations are corrupted by random errors, filtering out the errors and extracting the most precise information possible, and then predicting the future path of the object from past behavior. They do so as accessibly as possible without significantly reducing rigor, giving students a solid background in such prerequisites for studying filtering and prediction as series, probability concepts and conditioning in the discrete case. With plenty of examples and exercises, they cover Markov chains, filtering of discrete Markov chains, conditional expectations, filtering of continuous-space Markov chains, Wiener process and continuous time filtering, stationary sequences and prediction of stationary sequences. The result is a handy guide not only for students but for nonspecialist professionals or those who need to review basic concepts. Annotation �2007 Book News, Inc., Portland, OR (booknews.com)

Review

"The book is written in an elementary way but it is still mathematically rigorous. The book can be recommended to all students interested in stochastic models." ---- EMS Newsletter

"The book is well-written and provides a very nice basis for lecturing about this topic." ---- Zentralblatt MATH

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