Mapping and Colouring: Poisson Processes

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Related lectures (32)

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Poisson Process Mapping

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

Extreme Value Theory: Point Processes

Covers the application of extreme value theory to point processes and the estimation of extreme events from equally-spaced time series.

Estimating R: Marking and Convergence

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

Estimating R: Theory of Poisson Processes

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

Simulation & Optimization: Poisson Process & Random Numbers

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

Extreme Statistics: Threshold Models

Covers the theory and applications of extreme statistics, focusing on threshold models for analyzing extremes of time series.

Poisson Process Approach

Explores the Poisson process approach in extreme value analysis, emphasizing component-wise transformations and likelihood functions for extreme events.

Point Processes: Extremal Limit Theorems

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

Poisson Process Theory: Properties and Applications

Explores Poisson process theory, covering properties, applications, and key theorems.

Poisson Process: Density Theory and Applications

Explores Poisson processes, joint density, independence of events, and likelihood estimation.