Inhalte

Probability theory is a powerful tool for inferring the value of missing variables given a set of other variables. As the number of variables in a system increases, the joint probability distribution over these variables becomes overwhelmingly large. In this lecture we examine the implications of factoring one large joint probability distribution into a set of smaller conditional distributions and study suitable algorithms for inference.

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The main aspects of this lectrue are:

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• Probability theorie
• DAGs and Bayesian Networks
• Discrete inference
• Inference with continuous random variables
• Approximate inference with sampling
• Learning Bayesian Networks from data

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Hinweis: Diese Vorlesung kann nur noch im Master eingebracht werden.

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?bungen

Es erscheint w?chentlich ein ?bungsblatt zu den behandelten Vorlesungsinhalten. Jedes ?bungsblatt wird in der Globalübung?der folgenden Woche besprochen. Es gibt keine Abgabe / Korrektur von ?bungsbl?ttern.

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Literatur

• Hauptreferenz: Richard E. Neapolitan.?Learning Bayesian Networks. Prentice Hall Series in Artifical Intelligence, 2004.?ISBN 0-13-012534-2

• Daphne Koller, Nir Friedman.?Probabilistic Graphical Models: Principles and Techniques. The MIT Press, 2009.?ISBN 978-0262013192

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