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Covers methods to define the design storm, empirical distribution of rainfall maxima, Gumbel distribution, and intensity-duration-frequency relationships.
Explores generalization error in machine learning, focusing on data distribution and hypothesis impact.
Explores diffusion models, focusing on generating samples from a distribution and the importance of denoising in the process.
Explores techniques for estimating GEV parameters using graphical and likelihood-based methods, illustrated with real-world examples.
Explores the Carpan collector problem, analyzing expected completion times and waiting times for collecting different objects uniformly at random.
Covers the binary choice model, error term assumptions, specific constants, invariances, and distribution properties.
Explores the benefits of deeper networks in deep learning and the importance of over-parameterization and generalization.
Explores Extremal Types and Exceedance Theorems, GEV, GPD, stability, and limitations in extreme value modeling.
Covers Markov chains, Monte Carlo sampling, isotropy, and the curse of dimensionality.
Covers extremal limit theorems, basic statistical analysis, and applications to multivariate extremes, emphasizing the importance of understanding the distribution of maxima.
