Graphical Models: Probability Distributions and Factor Graphs

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Introduces elementary graph algorithms, focusing on Breadth-First Search and Depth-First Search.

Covers the basics of graph algorithms, focusing on modeling and representation of graphs in memory.

Explores Belief Propagation for graph coloring and its convergence properties.

Explores properties of matrices, irreducibility, and graph connectivity in networked control systems.

Explores pseudorandomness and the Expander Mixing Lemma in the context of d-regular graphs.

Introduces graph theory, network flows, and flow conservation laws with practical examples and theorems.

Explores the Stein algorithm for polynomial identity testing and the minimization of a cut problem.

Introduces the probabilistic method to prove existence results in graph theory.

Explores the transition from sparse to dense graphs and their real-world applications.

Covers stochastic properties, network structures, models, statistics, centrality measures, and sampling methods in network data analysis.