Poisson Process Theory: Properties and Applications

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Stochastic Simulation: Markov Processes Generation

Covers the generation of Markov processes and Poisson processes in stochastic simulation.

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.

Poisson Processes Theorems

Discusses important theorems related to Poisson processes and their applications in analyzing exceedances and likelihood.

Mapping Theorems: Poisson Processes and Intensity Functions

Explores mapping theorems for Poisson processes and their intensity functions.

Point Processes: Extreme Value Theory

Explores point processes in extreme value theory, focusing on modeling exceedances and the theory behind point patterns.

Poisson Process Approach

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

Simulation: Control of Dynamic Systems

Explores simulation of common structures and control of dynamic systems through friction and elasticity constants.

Asymptotic Independence Models

Explores extremal limit theorems, statistical analysis, and asymptotic independence models for rare events.

Extreme Statistics: Threshold Models

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

Stochastic Calculus: Lecture 1

Covers the essentials of probability, algebras, and conditional probability, including the Borel o-algebra and Poisson processes.