Stochastic Blockmodel Estimation

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Explores handling network data, including types of graphs, real-world network properties, and node importance measurement.

Basic Principles of Point Estimation

Explores the Method of Moments, Bias-Variance tradeoff, Consistency, Plug-In Principle, and Likelihood Principle in point estimation.

Optimality in Decision Theory: Unbiased Estimation

Explores optimality in decision theory and unbiased estimation, emphasizing sufficiency, completeness, and lower bounds for risk.

Confidence Intervals: Definition and Estimation

Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.

Statistical Analysis of Networks: Link Prediction and Biclustering

Explores link prediction, logistic regression, causal inference, and biclustering in statistical network analysis.

Statistical Theory: Inference and Optimality

Explores constructing confidence regions, inverting hypothesis tests, and the pivotal method, emphasizing the importance of likelihood methods in statistical inference.

Binary Covariate Impact: 2x2 Contingency Tables

Explores the impact of a binary covariate on binary responses using 2x2 tables.

Probability and Statistics

Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.

Fixed Points in Graph Theory

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

Cayley Graphs: Properties and Applications

Explores the properties and applications of Cayley graphs in group theory and network analysis.