Inference for Stochastic Processes: Large Networks Analysis

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Statistical Analysis of Network Data: Structures and Models

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

Probability and Statistics: Basics and Applications

Covers fundamental concepts of probability and statistics, focusing on data analysis, graphical representation, and practical applications.

Variational Inference and Neural Networks

Covers variational inference and neural networks for classification tasks.

Bayesian Estimation: Unsupervised Learning & MCMC

Explores Bayesian estimation for unsupervised learning and MCMC, using a Spin Glass Card game example.

Social and Information Networks: Structure

Explores the structure of social and information networks, focusing on giant components, clustering, tie formation, and network connectivity.

Estimation Methods in Probability and Statistics

Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.

Networks and Brain Networks

Covers the basics of networks, focusing on brain networks, historical breakthroughs, small-world and scale-free network discoveries, and the importance of the human connectome.

Exponential Family

Covers the properties of the exponential family and the estimation of parameters.

Eliminating Nuisance Parameters: Lemmas in Statistical Inference

Explores the elimination of nuisance parameters in statistical models using Lemmas 14 and 15.

Graphical Models: Representing Probabilistic Distributions

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