Anbieter: Hay-on-Wye Booksellers, Hay-on-Wye, HEREF, Vereinigtes Königreich
EUR 30,06
Anzahl: 1 verfügbar
In den WarenkorbZustand: Very Good. Unused, some outer edges have minor scuffs, cover has light scratches, some outer pages have marks from shelf wear, book content is in like new condition.
Sprache: Spanisch
Verlag: Editorial Académica Española, 2011
ISBN 10: 3846566799 ISBN 13: 9783846566794
Anbieter: moluna, Greven, Deutschland
EUR 26,11
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In den WarenkorbZustand: New.
Sprache: Englisch
Verlag: Cambridge University Press, 2011
ISBN 10: 0521196760 ISBN 13: 9780521196765
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 148,28
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New. In.
Sprache: Englisch
Verlag: Cambridge University Press, 2011
ISBN 10: 0521196760 ISBN 13: 9780521196765
Anbieter: Kennys Bookstore, Olney, MD, USA
EUR 199,30
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New. The first unified treatment of time series modelling techniques spanning machine learning, statistics, engineering and computer science. Editor(s): Barber, David; Cemgil, A. Taylan; Chiappa, Silvia. Num Pages: 432 pages, 135 b/w illus. 25 tables. BIC Classification: PBT. Category: (U) Tertiary Education (US: College). Dimension: 248 x 181 x 26. Weight in Grams: 914. . 2011. New. hardcover. . . . . Books ship from the US and Ireland.
Sprache: Englisch
Verlag: Cambridge University Press, 2011
ISBN 10: 0521196760 ISBN 13: 9780521196765
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - '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 probability, but no experience with time series analysis, are guided from fundamental concepts to the state-of-the-art in research and practice.