Message passing in graphical models

Related lectures (32)

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Explores interlacing families, Ramanujan graphs, and their construction using signed adjacency matrices.

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

Discusses Union-Find data structures and Minimum Spanning Trees, covering algorithms and their applications in network design and optimization.

Focuses on fixed points in graph theory and their implications in algorithms and analysis.

Covers the basics of information theory, entropy, and fixed points in graph colorings and the Ising model.

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

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Explores coordination in networked control systems, graph theory, and consensus algorithms.

Covers linear programming duality and complementary slackness condition.

Explores the Szemerédi Regularity Lemma, e-regularity in bipartite graphs, supergraph structure, and induction techniques.