Bivariate Maxima: Parametric Models and Distributions

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Covers probability distributions, moments, and continuous random variables.

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Covers distributions, derivatives, convergence, and continuity criteria in function spaces.

Explores mixture models, including discrete and continuous mixtures, and their application in capturing taste heterogeneity in populations.

Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.

Covers the basics of statistical theory, including probability models, random variables, and sampling distributions.

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.

Covers the concepts of compression and prediction using prefix-free codes and distributions.

Covers fundamental statistics concepts, including estimation theory, distributions, and the law of large numbers, with practical examples.

Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.