Modeling Extremal Processes

Related lectures (31)

Page 2 of 4

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Covers statistical inference, machine learning, SVMs for spam classification, email preprocessing, and feature extraction.

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

Covers model selection, nuisance parameters, and higher-order inference methods in statistical inference.

Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.

Covers the construction of confidence intervals for a normal distribution with unknown mean and variance.

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

Covers the construction of approximate confidence intervals using the central limit theorem for large sample sizes.

Explores statistical applications of threshold exceedances and GPD modeling in extreme value theory.

Explores the theory of point processes and their applications to extremes, emphasizing the Laplace functional and Kallenberg's theorem.