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Bayesian Time Series Models

£89.99

David Barber, A. Taylan Cemgil, Silvia Chiappa, Yves Atchadé, Gersende Fort, Eric Moulines, Pierre Priouret, Nick Whiteley, Adam M. Johansen, Omiros Papaspiliopoulos, Richard Eric Turner, Maneesh Sahani, Cédric Archambeau, Manfred Opper, Onno Zoeter, Tom Heskes, Idris A. Eckley, Paul Fearnhead, Rebecca Killick, Sumeetpal S. Singh, Simon J. Godsill, Sze Kim Pang, Jack Li, François Septier, Simon Hill, Risi Kondor, John A. Quinn, Christopher K. I. Williams, Michalis K. Titsias, Magnus Rattray, Neil D. Lawrence, Jurgen Van Gael, Zoubin Ghahramani, Michael A. Osborne, Alex Rogers, Stephen J. Roberts, Sarvapali D. Ramchurn, Nick R. Jennings, Hilbert J. Kappen, Marc Toussaint, Amos Storkey, Stefan Harmeling
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  • Date Published: August 2011
  • availability: Available
  • format: Hardback
  • isbn: 9780521196765

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About the Authors
  • '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.

    • The first unified treatment of the emerging knowledge-base in Bayesian time series techniques
    • Real-world examples range from bioinformatics to control theory
    • Treats classical models as well as the more advanced
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    Reviews & endorsements

    'This volume is an ambitious attempt to bring researchers from many areas together into a common theme and exhibits well the challenges of such efforts in terms of finding a common ground or terminology. The book is well organized and the contributors provide highly technical material with 'brea[d]th and depth' … The topics in the book are very broad and several of them go beyond the common theme of Bayesian time series. Perhaps an alternative title that would be more reflective of the contents of the book could be Highly Structured Probabilistic Modeling for Researchers Interested in Bayesian Methods, Modern Monte Carlo, and Time Series.' Gabriel Huerta, Journal of the American Statistical Association

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    Product details

    • Date Published: August 2011
    • format: Hardback
    • isbn: 9780521196765
    • length: 432 pages
    • dimensions: 246 x 180 x 28 mm
    • weight: 0.84kg
    • contains: 135 b/w illus. 25 tables
    • availability: Available
  • Table of Contents

    Contributors
    Preface
    1. Inference and estimation in probabilistic time series models David Barber, A. Taylan Cemgil and Silvia Chiappa
    Part I. Monte Carlo:
    2. Adaptive Markov chain Monte Carlo: theory and methods Yves Atchadé, Gersende Fort, Eric Moulines and Pierre Priouret
    3. Auxiliary particle filtering: recent developments Nick Whiteley and Adam M. Johansen
    4. Monte Carlo probabilistic inference for diffusion processes: a methodological framework Omiros Papaspiliopoulos
    Part II. Deterministic Approximations:
    5. Two problems with variational expectation maximisation for time series models Richard Eric Turner and Maneesh Sahani
    6. Approximate inference for continuous-time Markov processes Cédric Archambeau and Manfred Opper
    7. Expectation propagation and generalised EP methods for inference in switching linear dynamical systems Onno Zoeter and Tom Heskes
    8. Approximate inference in switching linear dynamical systems using Gaussian mixtures David Barber
    Part III. Change-Point Models:
    9. Analysis of change-point models Idris A. Eckley, Paul Fearnhead and Rebecca Killick
    Part IV. Multi-Object Models:
    10. Approximate likelihood estimation of static parameters in multi-target models Sumeetpal S. Singh, Nick Whiteley and Simon J. Godsill
    11. Sequential inference for dynamically evolving groups of objects Sze Kim Pang, Simon J. Godsill, Jack Li, François Septier and Simon Hill
    12. Non-commutative harmonic analysis in multi-object tracking Risi Kondor
    13. Physiological monitoring with factorial switching linear dynamical systems John A. Quinn and Christopher K. I. Williams
    Part V. Non-Parametric Models:
    14. Markov chain Monte Carlo algorithms for Gaussian processes Michalis K. Titsias, Magnus Rattray and Neil D. Lawrence
    15. Non-parametric hidden Markov models Jurgen Van Gael and Zoubin Ghahramani
    16. Bayesian Gaussian process models for multi-sensor time series prediction Michael A. Osborne, Alex Rogers, Stephen J. Roberts, Sarvapali D. Ramchurn and Nick R. Jennings
    Part VI. Agent Based Models:
    17. Optimal control theory and the linear Bellman equation Hilbert J. Kappen
    18. Expectation-maximisation methods for solving (PO)MDPs and optimal control problems Marc Toussaint, Amos Storkey and Stefan Harmeling
    Index.

  • Editors

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

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

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

    Contributors

    David Barber, A. Taylan Cemgil, Silvia Chiappa, Yves Atchadé, Gersende Fort, Eric Moulines, Pierre Priouret, Nick Whiteley, Adam M. Johansen, Omiros Papaspiliopoulos, Richard Eric Turner, Maneesh Sahani, Cédric Archambeau, Manfred Opper, Onno Zoeter, Tom Heskes, Idris A. Eckley, Paul Fearnhead, Rebecca Killick, Sumeetpal S. Singh, Simon J. Godsill, Sze Kim Pang, Jack Li, François Septier, Simon Hill, Risi Kondor, John A. Quinn, Christopher K. I. Williams, Michalis K. Titsias, Magnus Rattray, Neil D. Lawrence, Jurgen Van Gael, Zoubin Ghahramani, Michael A. Osborne, Alex Rogers, Stephen J. Roberts, Sarvapali D. Ramchurn, Nick R. Jennings, Hilbert J. Kappen, Marc Toussaint, Amos Storkey, Stefan Harmeling

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