Complete bipartite graph

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Covers the Marriage Problem and the Gale Shapley Algorithm to find stable matches.

Explores graphical models, factor graphs, and probabilistic inferences in complex systems.

Covers linear programming, weighted bipartite matching, and vertex cover problems in optimization.

Discusses graph algorithms, focusing on flow networks and strongly connected components.

Covers variational inference and neural networks for classification tasks.

Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.

Explores causal inference through learning graph structures for causal reasoning from observational data.

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

Covers complete and orthonormal families in a Hilbert space, discussing unique projections and vector decomposition.

Explores Markov chains, Ising Model, Metropolis algorithm, and Glauber dynamics.