bayesian time series models (17 Ergebnisse)

Hardcover. Zustand: Good. 0824779363 Good; Hardcover; 1988, CRC Press; Former library copy with standard library markings; Clean covers with minor shelfwear; Library stamps to endpapers; Text pages clean & unmarked; Good binding with straight spine; Green covers with title in white lettering; 576 pages; "Bayesian Analysis of Time Series and Dynamic Models (Statistics: A Series of Textbooks and Monographs)," by James Spall.

Zustand: New. In.

Zustand: New. In.

Zustand: 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.

Taschenbuch. Zustand: Neu. BAYESIAN INFERENCE FOR STRUCTURAL CHANGES IN TIME SERIES MODELS | MONOGRAPH ON TIME SERIES ANALYSIS | Venkatesan D (u. a.) | Taschenbuch | 120 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783844314922 | Verantwortliche Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Saarbrücken, info[at]akademikerverlag[dot]de | Anbieter: preigu.

Taschenbuch. Zustand: Neu. A BAYESIAN ANALYSIS OF CHANGING TIME SERIES MODELS | MONOGRAPH ON TIME SERIES ANALYSIS | Venkatesan D (u. a.) | Taschenbuch | 136 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783843393676 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Taschenbuch. Zustand: Neu. Bayesian Clustering of Categorical Time Series | An Approach Using Finite Mixtures of Markov Chain Models | Christoph Pamminger | Taschenbuch | Kartoniert / Broschiert | Englisch | 2013 | VDM Verlag Dr. Müller | EAN 9783836498050 | Verantwortliche Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Saarbrücken, info[at]akademikerverlag[dot]de | Anbieter: preigu.

Zustand: New. In.

Zustand: New. In.

Zustand: New. In.

Zustand: 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.

Taschenbuch. Zustand: Neu. Enhanced Bayesian Network Models for Spatial Time Series Prediction | Recent Research Trend in Data-Driven Predictive Analytics | Monidipa Das (u. a.) | Taschenbuch | xxiii | Englisch | 2020 | Springer | EAN 9783030277512 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This research monograph is highly contextual in the present era of spatial/spatio-temporal data explosion. The overall text contains many interesting results that are worth applying in practice, while it is also a source of intriguing and motivating questions for advanced research on spatial data science.The monograph is primarily prepared for graduate students of Computer Science, who wish to employ probabilistic graphical models, especially Bayesian networks (BNs), for applied research on spatial/spatio-temporal data. Students of any other discipline of engineering, science, and technology, will also find this monograph useful. Research students looking for a suitable problem for their MS or PhD thesis will also find this monograph beneficial. The open research problems as discussed with sufficient references in Chapter-8 and Chapter-9 can immensely help graduate researchers to identify topics of their own choice. The various illustrations and proofs presented throughout the monograph may help them to better understand the working principles of the models. The present monograph, containing sufficient description of the parameter learning and inference generation process for each enhanced BN model, can also serve as an algorithmic cookbook for the relevant system developers.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This research monograph is highly contextual in the present era of spatial/spatio-temporal data explosion. The overall text contains many interesting results that are worth applying in practice, while it is also a source of intriguing and motivating questions for advanced research on spatial data science.The monograph is primarily prepared for graduate students of Computer Science, who wish to employ probabilistic graphical models, especially Bayesian networks (BNs), for applied research on spatial/spatio-temporal data. Students of any other discipline of engineering, science, and technology, will also find this monograph useful. Research students looking for a suitable problem for their MS or PhD thesis will also find this monograph beneficial. The open research problems as discussed with sufficient references in Chapter-8 and Chapter-9 can immensely help graduate researchers to identify topics of their own choice. The various illustrations and proofs presented throughout the monograph may help them to better understand the working principles of the models. The present monograph, containing sufficient description of the parameter learning and inference generation process for each enhanced BN model, can also serve as an algorithmic cookbook for the relevant system developers.

Paperback. Zustand: Brand New. 176 pages. 9.25x6.10x0.42 inches. In Stock.

Hardcover. Zustand: Brand New. 172 pages. 9.25x6.10x0.59 inches. In Stock.

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.