Stochastic Blockmodel Estimation

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Explores statistical analysis of network data, covering graph structures, models, statistics, and sampling methods.

Covers the concepts of spin glasses and Bayesian estimation, focusing on observing and inferring information from a system closely.

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

Covers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.

Explores graph statistics, random graph generation, network analysis, centrality measures, and clustering coefficients.

Explores spectral clustering theory, eigenvalue decomposition, Laplacian matrix, and practical applications in identifying clusters.

Explores the application of matrices and eigendecompositions in networks.

Explores the Cramér-Rao bound, hypothesis testing, and optimality in statistical theory.

Explores clustering and dimensionality reduction techniques in finance to clean and simplify data.

Explores the diagonalization of matrices and the conditions for exact diagonalization, with examples demonstrating the process.