Animals and artificial systems alike are faced with the problem of making inferences about their environments and choosing appropriate responses based on incomplete, uncertain and noisy data.

Probabilistic models and algorithms are flourishing in both life sciences and information sciences as ways of understanding the behavior of subjects and the neural processing underlying this behavior, and building robots and artificial agents that can function effectively in such circumstances.

The objective of this winter school is to present the latest advances in this subject, specifically addressing the following topics:

  •  Probability theory as an alternative to logic (Pierre Bessière - CNRS, Grenoble)
  •  Satistical learning (Samy Bengio - Google, Mountain View)
  •  Probabilistic models of Central Nervous System (Sophie Denève - Ecole Normal Supérieur, Paris)
  •  Approximate evaluation of Bayesian calculus (Vaclav Smidl - UTIA, Prague)
  •  Probabilistic Robotics (Wolfram Burgard - Universität Freiburg)
  •  Probabilistic interpretation of physiological and psychophysical data (Jacques Droulez - Collège de France, Paris)
  •  Industrial applications (Emmanuel Mazer - ProBAYES, Grenoble)

    This winter school is a prolongation of the Bayesian Cognition workshop held in Paris in January 2006.