Bivariate Maxima: Parametric Models and Distributions

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Page 3 of 3

Delves into the binary choice model, error term specification, and Extreme Value distribution properties.

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Explores denoising signals with Gaussian mixture models and EM algorithm, EMG signal analysis, and image segmentation using Markovian models.

Discusses variance and covariance calculations, transformations of random variables, and mixtures in probability and statistics.

Explores air pollution analysis using wind data, probability distributions, and trajectory models for air quality assessment.

Covers descriptive statistics, hypothesis testing, and correlation analysis with various probability distributions and robust statistics.

Discusses vectors of random variables and empirical distributions, including their properties and significance in statistics.

Covers the estimation of distributions using samples and probability models.

Explores continuous random variables, density functions, joint variables, independence, and conditional densities.

Discusses parameter estimation, including checks, quality, distribution, and statistical properties of estimates.