Statistical Analysis of Network Data

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

Covers handling network data, types of graphs, centrality measures, and properties of real-world networks.

Introduces network data structures, models, and analysis techniques, emphasizing permutation invariance and Erdős-Rényi networks.

Covers the fundamentals of handling networks and centrality measures in graph theory.

Explores statistical analysis of network data, covering noisy sampled networks, likelihood estimation, multilayer networks, and directed networks.

Explores graph theory concepts, centrality measures, and real-world network properties, providing insights into handling diverse types of networks.

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

Explores scale-free networks, power laws, preferential attachment, and network assortativity.

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

Covers Maximum Likelihood Estimation, focusing on ML Estimation-Distribution, Shrinkage Estimation, and Loss functions.