Defining and working with probabilistic models presents a number of challenges, both conceptual and computational. Recent work in computer science and statistics has provided a variety of tools for addressing these challenges, one of which is the language of graphical models.
Graphical models represent probability distributions using a simple graph-based semantics, making it easy to represent and reason about complex, structured probability distributions. I will outline the basic ideas behind graphical models, illustrating how they can be used to support sophisticated probabilistic models of cognition.
Presentation (PowerPoint File)
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