Graphical models: Inference and Factor Graphs

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

Covers graphical models for probability distributions and factor graphs representation.

Introduces network data structures, models, and analysis techniques, emphasizing permutation invariance and Erdős-Rényi networks.

Explores expanders, Ramanujan graphs, eigenvalues, Laplacian matrices, and spectral properties.

Covers handling network data, types of graphs, centrality measures, and properties of real-world networks.

Explores Belief Propagation in graphical models, factor graphs, spin glass examples, Boltzmann distributions, and graph coloring properties.

Explores handling network data, including types of graphs, real-world network properties, and node importance measurement.

Covers graph algorithms, modeling relationships between objects, and traversal techniques like BFS and DFS.

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

Covers recent advancements in the dimer model, emphasizing its applications in probability and conformal field theory.