Theme of Workshop:
Time-series analysis throws up interesting problems which remain fundamental to several key and as yet unsolved application areas. For example, Bayesian time-series models typically couple all time-points of the series, resulting in intractable inference in high-dimensional latent spaces and therefore requiring approximation.
The workshop will discuss both theories and applications related to probabilistic approaches to time-series analysis. Viewpoints and experiences from researchers belonging to different communities, including machine learning, statistics and statistical physics, are particularly encouraged. For example, approximate inference in the machine learning community tends to be more focussed on deterministic/variational approaches, whilst the statistics community tends to prefer sampling approaches. Amongst others, discussions related to this topic would be appreciated.
More generally, advances in practical and theoretical issues related to probabilistic approaches to time-series modelling, including for example inference, estimation, prediction, classification, clustering and source separation, are appreciated. Novel application areas and the challenges that they bring are also welcome.
Confirmed Invited Speakers:
- Prof. Zoubin Ghahramani, University of Cambridge
- Prof. Simon Godsill, University of Cambridge
- Prof. Eric Moulines, ENST Paris
- Prof. Manfred Opper, TU Berlin
- Dr. Omiros Papaspiliopoulos, Universitat Pompeu Fabra Barcelona
- Dr. Sumeetpal Singh, University of Cambridge
- Prof. Chris Williams, University of Edinburgh
To contribute to the workshop with a talk or poster presentation, it is required to submit a paper, limited to a maximum of 8 pages, by 11 April 2008. Acceptance will be notified by 28 April 2008.
We intend to publish a peer reviewed book that will summarise the major contributions to the workshop. Authors of papers presented at the workshop will be invited to submit a version to be published in the book.
Some funding is available for participants to partially cover the costs of the workshop.