Stochastic Block Model

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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.