Stochastic Block Model

Related lectures (32)

Page 1 of 4

Explores Belief Propagation for graph coloring and its convergence properties.

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

Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.

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

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

Explores the Cavity Method in Mean Field Theory, analyzing spins in an external field within a graph.

Covers graphical models for probability distributions and factor graphs representation.

Explains message passing in graphical models and the matching problem in graph theory.

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.

Covers universality and rotational invariance in isoradial graphs and their implications in statistical mechanics.