Rare Events and Extreme Modeling

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Covers a simple mathematical model for supervised learning with k-nearest neighbors in regression.

Covers Extreme Value Theory, focusing on GEV and GPD distributions and the POT Model for threshold exceedances.

Covers the Stochastic Block Model and its application in community detection, exploring its mathematical formulation and challenges.

Covers the magnetic thickness, short and long junctions, and critical current.

Introduces the basics of probability models, covering random variables, distributions, and statistical estimation.

Covers free fields and probabilistic functions, focusing on random variables and their properties.

Explores high-dimensional perspectives in machine learning theory, shedding light on new insights and challenges.

Covers the verification and validation process in numerical flow simulation, ensuring credibility of simulation outcomes.

Explores statistical estimation, regression models, and model selection in parametric models.

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.