Graphical Models: Representing Probabilistic Distributions

In course

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Description

This lecture introduces graphical models as graphical representations of probabilistic distributions, starting with graphs, nodes, edges, and adjacency matrices. It covers factor graphs, computational problems, and statistical physics applications.

Instructors (2)

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Official source

https://mediaspace.epfl.ch/media/0_y40t48z6

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Ontological neighbourhood

Mathematics

Discrete mathematics: Graph theory

Mathematical logic: Set theory, Boolean logic

Probability theory: Topics in probability theory

Statistics

Statistical inference: Mathematical statistics

Physics

Condensed matter physics: Phase transitions

Electromagnetism: Electrical networks

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