Compound Poisson distribution

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Explores theory and applications of multivariate extremes, emphasizing fitting marginal and dependence models together for accurate estimation.

Explores Poisson processes, Pickands' function, and multivariate angular densities.

Explores Bayes' Theorem for defective parts detection, discrete random variables, and distribution functions, with practical examples and exercises.

Covers the estimation of R in Poisson processes, focusing on marking points and convergence.

Covers statistical concepts like probability, estimation, hypothesis testing, and confidence intervals for mathematicians.

Explains moments, expectation, variance, and quantiles in probability distributions with examples like Poisson and exponential distributions.

Covers arrival and departure processes, Poisson distributions, service times, and system performance evaluation.

Covers the Exponential Family, including its definition, sufficient statistics, and distribution properties.

Explores the application of linear algebra in data science, covering variance reduction, model distribution theory, and maximum likelihood estimates.

Covers moment and cumulant generating functions for random variables, enabling calculation of moments and cumulants.