Description:
Cambridge University Press, 2011. 1. Hardcover. Very Good. Very Good Minus; Hardcover; Covers are still glossy with a few light scratches; Unblemished textblock edges; The endpapers and all text pages are clean and unmarked; The binding is excellent with a straight spine; This book will be shipped in a sturdy cardboard box with foam padding; Medium-Large Format (Quatro, 9.75" - 10.75" tall); White, purple, and orange covers with title in black lettering; 2011, Cambridge University Press; 432 pages; "Bayesian Time Series Models," by David Barber.
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Bayesian Time Series Models Hardcover - 2011
by David Barber (Editor); A. Taylan Cemgil (Editor); Silvia Chiappa (Editor)
Details
- Title Bayesian Time Series Models
- Author David Barber (Editor); A. Taylan Cemgil (Editor); Silvia Chiappa (Editor)
- Binding Hardcover
- Pages 432
- Volumes 1
- Language ENG
- Publisher Cambridge University Press
- Date 2011-09-30
- Illustrated Yes
- Features Bibliography, Illustrated, Index, Table of Contents
- ISBN 9780521196765 / 0521196760
- Weight 1.85 lbs (0.84 kg)
- Dimensions 9.7 x 7.1 x 1.1 in (24.64 x 18.03 x 2.79 cm)
- Library of Congress Catalog Number 2011008051
- Dewey Decimal Code 519.55
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Bayesian Time Series Models [Hardcover] Barber, David
by David Barber
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Bayesian Time Series Models
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Cambridge University Press CUP , pp. xiii + 417 . Hardback. New.
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Bayesian Time Series Models by Barber, David
- Used
- Hardcover
- Condition
- Used
- Binding
- Hardcover
- ISBN 10 / ISBN 13
- 9780521196765 / 0521196760
- Quantity Available
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ISBN: 9780521196765Cambridge University Press, 01 January 2011
Hardback, 417 pages
'What's going to happen next?' Time series data hold the answers, and Bayesian methods represent the cutting edge in learning what they have to say. This ambitious book is the first unified treatment of the emerging knowledge-base in Bayesian time series techniques. Exploiting the unifying framework of probabilistic graphical models, the book covers approximation schemes, both Monte Carlo and deterministic, and introduces switching, multi-object, non-parametric and agent-based models in a variety of application environments. It demonstrates that the basic framework supports the rapid creation of models tailored to specific applications and gives insight into the computational complexity of their implementation. The authors span traditional disciplines such as statistics and engineering and the more recently established areas of machine learning and pattern recognition. Readers with a basic understanding of applied… Read More
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