Graphical Models: Probability Distributions and Factor Graphs

Related lectures (29)

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Explores Markov chains, focusing on the coloring problem and algorithm analysis.

Explores graph traversal methods, spanning trees, and shortest paths using BFS and DFS.

Introduces Spectral Graph Theory, exploring eigenvalues and eigenvectors' role in graph properties.

Explores the role of graphs in deep learning, focusing on their structure, applications, and techniques for processing graph data.

Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.

Introduces the basics of graphs and networks, covering definitions, paths, trees, flows, circulation, and spanning trees.

Explores Prim's algorithm for minimum spanning trees and introduces the Traveling Salesman Problem.

Explores Cheeger's inequalities for random walks on graphs and their implications.

Explores the connection between graphs and probabilities, emphasizing modular and super modular probabilities and correlation properties.