Inference for Max-Stable Processes

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Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

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

Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of feature expansion and kernel methods.

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

Analyzes Venice sea levels from 1887 to 2017, focusing on extreme values and fitting various models to the data.

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Covers the concept of linear regression, including polynomial regression and hyperparameters selection.

Explores statistical inference for linear models, covering model fitting, parameter estimation, and variance decomposition.

Covers model complexity, overfitting, and strategies to select appropriate machine learning models.

Explores linear models, overfitting, and the importance of feature expansion and adding more data to reduce overfitting.