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Cognitive Science

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Graphical Models. By Kevin Murphy, 1998.

Graphical Models

"Graphical models are a marriage between probability theory and graph theory. They provide a natural tool for dealing with two problems that occur throughout applied mathematics and engineering -- uncertainty and complexity -- and in particular they are playing an increasingly important role in the design and analysis of machine learning algorithms. Fundamental to the idea of a graphical model is the notion of modularity -- a complex system is built by combining simpler parts. Probability theory provides the glue whereby the parts are combined, ensuring that the system as a whole is consistent, and providing ways to interface models to data. The graph theoretic side of graphical models provides both an intuitively appealing interface by which humans can model highly-interacting sets of variables as well as a data structure that lends itself naturally to the design of efficient general-purpose algorithms.

Infer.NET. Graphical Models. Cognitive Science Literature. This is a list of cognitive modeling papers solicited from a wide range of cognitive modelers, by asking them the following: "I wonder if you would do me the honor of sending me a list of your top 2-5 favorite cognitive modeling papers.

Cognitive Science Literature

I would expect that 1-3 of these would be your papers, and 1-3 would be someone else's. I am looking for papers where someone really nailed the phenomenon, whatever it is.