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14 results

Bayesian Time Series Models
  • Edited by David Barber, A. Taylan Cemgil, Silvia Chiappa
  • Published online:
    07 September 2011
    Print publication:
    11 August 2011
  • '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.

  • Preface

  • Edited by David Barber, University College London, A. Taylan Cemgil, Boğaziçi Üniversitesi, Istanbul, Silvia Chiappa, University of Cambridge
  • Book:
    Bayesian Time Series Models
    Published online:
    07 September 2011
    Print publication:
    11 August 2011, pp xi-xiv

  • Summary

    Probabilistic time series modelling

    Time series are studied in a variety of disciplines and appear in many modern applications such as financial time series prediction, video-tracking, music analysis, control and genetic sequence analysis. This widespread interest at times obscures the commonalities in the developed models and techniques. A central aim of this book is to attempt to make modern time series techniques, specifically those based on probabilistic modelling, accessible to a broad range of researchers.

    In order to achieve this goal, leading researchers that span the more traditional disciplines of statistics, control theory, engineering and signal processing, and the more recent areas of machine learning and pattern recognition, have been brought together to discuss advancements and developments in their respective fields. In addition, the book makes extensive use of the graphical models framework. This framework facilitates the representation of many classical models and provides insight into the computational complexity of their implementation. Furthermore, it enables to easily envisage new models tailored for a particular environment. For example, the book discusses novel state space models and their application in signal processing including condition monitoring and tracking. The book also describes modern developments in the machine learning community applied to more traditional areas of control theory.

    The effective application of probabilistic models in the real world is gaining pace, largely through increased computational power which brings more general models into consideration through carefully developed implementations.