B10: Bayesian Methods and Generative Models

B10: Bayesian Methods and Generative Models J Fiser
Central European University Budapest, Hungary
In the last two decades, a quiet revolution took place in vision research, in which Bayesian methods replaced the once-dominant signal detection framework as the most suitable approach to modeling visual perception and learning. This tutorial will review the most important aspects of this new framework from the point of view of vision scientists. We will start with a motivation as to why Bayes, then continue with a quick overview of the basic concepts (uncertainty and probabilistic representations, basic equations), moving on to the main logic and ingredients of generative models including Bayesian estimation, typical generative models, belief propagation, and sampling methods. Next we will go over in detail of some celebrated examples of Bayesian modeling to see the argument and implementation of the probabilistic framework in action. Finally, we will have an outlook as to what the potential of the generative framework is to capture vision, and what the new challenges are to be resolved by the next generation of modelers.

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