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Probabilistic Models for Linear Regression

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Bayesian Estimation: Unsupervised Learning & MCMC

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

Statistical Theory: Inference and Optimality

Explores constructing confidence regions, inverting hypothesis tests, and the pivotal method, emphasizing the importance of likelihood methods in statistical inference.

Statistical Inference: Model Selection and Nuisance Parameters

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

Modeling Extremal Processes

Explores extremal limit theorems, Gaussian processes, and modeling rare events in spatial problems and time series.