Apologies for cross-posting.
CALL FOR ABSTRACTS: ==================================================== NIPS 2006 Workshop on Dynamical Systems, Stochastic Processes and Bayesian Inference ==================================================== http://www.cs.ucl.ac.uk/staff/c.archambeau/dsb.htm December 8-9, Whistler, BC, Canada [Abstract submission deadline: November 1, 2006] OVERVIEW: The modelling of continuous-time dynamical systems from uncertain observations is an important task that comes up in a wide range of applications ranging from numerical weather prediction over finance to genetic networks and motion capture in video. Often, we may assume that the dynamical models are formulated by systems of differential equations. In a Bayesian approach, we may then incorporate a priori knowledge about the dynamics by providing probability distributions on the unknown functions, which correspond for example to driving forces and appear as coefficients or parameters in the differential equations. Hence, such functions become stochastic processes in a probabilistic Bayesian framework. Gaussian processes (GPs) provide a natural and flexible framework in such circumstances. The use of GPs in the learning of functions from data is now a well-established technique in Machine Learning. Nevertheless, their application to dynamical systems becomes highly nontrivial when the dynamics is nonlinear in the (Gaussian) parameter functions. This happens naturally for nonlinear systems which are driven by a Gaussian noise process, or when the nonlinearity is needed to provide necessary constraints (e.g., positivity) for the parameter functions. In such a case, the prior process over the system's dynamics is non-Gaussian right from the start. This means, that closed form analytical posterior predictions (even in the case of Gaussian observation noise) are no longer possible. Moreover, their computation requires the entire underlying Gaussian latent process at all times (not just at the discrete observation times). Hence, inference of the dynamics would require nontrivial sampling methods or approximation techniques. This raises the following questions: - What is the practical relevance of nonlinear effects, i.e. could we just ignore them? - How should we sample randomly from posterior continuous-time processes? - How should we deal with large data sets and/or very high dimensional data? - Are functional Laplace approximations suitable? - Can we think of variational approximations? - Can we do parameter and hyper-parameter estimation? - Etc. The aim of this workshop is to provide a forum for discussing open problems related to continuous-time stochastic dynamical systems, their links to Bayesian inference and their relevance to Machine Learning. The workshop will be of interest to workers in both Bayesian Inference and Stochastic Processes. We hope that the workshop will provide new insights in continous-time stochastic processes and serve as a starting point for new research perspectives and future collaborations. SUBMISSIONS: We welcome extended abstract submissions to the NIPS 2006 workshop on "Dynamical Systems, Stochastic Processes and Bayesian Inference" in the following related areas (but not restricted to): - Nonlinear dynamical systems - Bayesian inference in stochastic processes - Gaussian and non-Gaussian processes - Continuous-time Markov chains - Continuous-time discrete/continuous state processes - Gaussian, mixture of Gaussians and nonparametric belief networks - Nonlinear filtering/smoothing The suggested abstract length is 4 pages (maximum 8 pages), formatted in the NIPS format. The abstracts will be made available on the web. The authors should submit their extended abstract to [EMAIL PROTECTED] in PDF before Nov. 1, 2006, 23:59 UTC. An email confirming the reception of the submission will be sent by the organizers. Further requests, suggestions and comments should be sent to [EMAIL PROTECTED] SCHEDULE: Oct. 01: Call for extended abstracts Nov. 01: Abstract submission deadline Nov. 17: Notification of acceptance Nov. 24: Final extended abstracts due Dec. 8 or 9: Workshop PROGRAM: In order to encourage an active participation of the attendees, both, the morning and the afternoon session will include invited talks, short peer-reviewed spotlights presentations, and extended poster sessions for informal discussions. The workshop will close with a wrap-up. SPEAKERS: Neil Lawrence, University of Sheffield. Manfred Opper, Technical University Berlin. Chris Williams, University of Edingburgh. ORGANIZERS: Cedric Archambeau, University College, London. Manfred Opper, Technical University, Berlin. John Shawe-Taylor, University College, London. PROGRAM COMMITTEE: Cedric Archambeau, University College, London. Dan Cornford, Aston University. Manfred Opper, Technical University, Berlin. John Shawe-Taylor, University College, London. Magnus Rattray, University of Manchester. _______________________________________________ uai mailing list uai@ENGR.ORST.EDU https://secure.engr.oregonstate.edu/mailman/listinfo/uai
