Bayesian Time Series Models

David Barber

ISBN 10: 0521196760 / ISBN 13: 9780521196765

Published by Cambridge University Press, 2011

About this Item

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.

Seller Inventory # 9780521196765

Bibliographic Details

• Title: Bayesian Time Series Models
• Publisher: Cambridge University Press
• Publication Date: 2011
• Language: English
• ISBN 10: 0521196760
• ISBN 13: 9780521196765
• Binding: Buch
• Condition: Neu
• Dimensions: 250W x 175H x 28D millimeters 250 millimeters width by 175 millimeters height by 28 millimeters depth
• Weight: 920 grams

About this title

Synopsis

'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.

About the Authors

David Barber is a Reader in Information Processing at University College London.

A. Taylan Cemgil is an Assistant Professor in the Department of Computer Engineering at Boğaziçi University, Istanbul.

Silvia Chiappa is a Marie Curie Fellow at the Statistical Laboratory, Cambridge.