Inference for Stochastic Processes: Large Networks Analysis

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Statistical Analysis of Network Data: Noisy Sampled Networks

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

Statistical analysis of network data

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

Probabilistic Models for Linear Regression

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Maximum Likelihood: Inference and Model Comparison

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.

Epidemic Spreading Models

Covers classical models of epidemic spreading and dynamics on networks with examples.

Statistical Analysis of Network Data

Explores epidemics in network data, covering SIR model, basic reproductive ratio, percolation, directed networks, and maximum likelihood estimation.

Parameter Estimation

Introduces statistical inference concepts, focusing on parameter estimation, unbiased estimators, and mean estimation using independent random variables.

Spin Glasses and Bayesian Estimation

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

Estimation and Method of Moments

Covers the definition of statistics and estimators, examples of estimators, and the method of moments.

Handling Network Data

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