Graphical Models: Probability Distributions and Factor Graphs

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

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

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

Covers graph properties, representations, and traversal algorithms using BFS and DFS.

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

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

Explores graph coloring, directed cycles, LLL algorithm applications, and element dependencies in graphs.

Covers girth, independence, probability, union bound, sets, and hypergraph recoloring.

Compares random and symmetrical graph coloring in terms of cluster colorability and equilibrium.

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