Lectures related to Stochastic Block Model

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.

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

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

Explores the convergence of random walks on graphs and the properties of weighted adjacency matrices.

Explores knowledge inference for graphs, discussing label propagation, optimization objectives, and probabilistic behavior.

Explores Cheeger's inequalities for random walks on graphs and their implications.

Covers the properties of the exponential family and the estimation of parameters.

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

Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.

Explores Gaussian mixture models and denoising noisy signals using a probabilistic approach.

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.