Poisson Process Theory: Properties and Applications

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Explores simulation pitfalls, random numbers, discrete & continuous distributions, and Monte-Carlo integration.

Explores Gaussian processes, covariance functions, intrinsic stationarity, and extreme applications in statistics.

Covers the theorems of superposition and colouring for Poisson processes.

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

Covers the theory of Poisson processes and the estimation of the intensity parameter R.

Explains how q = rw defines a Poisson process and its intensity.

Explores point processes in spatial analysis, focusing on spatial object dissemination and pattern detection.

Covers point processes, convergence criteria, Laplace functionals, Gaussian processes, covariance functions, and intrinsic stationarity.

Explores multivariate extremes, including overwhelming sea defenses and heat waves.

Explores the theory of point processes and their applications to extremes, emphasizing the Laplace functional and Kallenberg's theorem.