Lectures related to Poisson point process

Point Processes: Spatial Analysis

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

Simulation & Optimization: Poisson Process & Random Numbers

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

Mapping and Colouring: Poisson Processes

Covers the theorems of superposition and colouring for Poisson processes.

Estimating R: Marking and Convergence

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

Point Processes: Extremal Limit Theorems

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

Mapping Theorems: Poisson Processes and Intensity Functions

Explores mapping theorems for Poisson processes and their intensity functions.

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 Process: Probability Law

Covers the Poisson process in detail, focusing on the probability law and its applications.

Multivariate Extremes: Applications and Dependence

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

Poisson Process Theory: Properties and Applications

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

Stochastic Simulation: Markov Processes Generation

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

Poisson Process Approach

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

Poisson Processes Theorems

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

Statistics of Multivariate Extremes: Inference and Models

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

Extreme Statistics: Threshold Models

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

Poisson Process: Probability Law

Covers the Poisson process, a stochastic model for communications, focusing on the probability law.

Point Processes: Extreme Value Theory

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

Escape noise

Explores escape noise in computational neuroscience, covering stochastic intensity, interspike intervals, likelihood functions, noise model comparison, and rate versus temporal codes.

Stochastic Models for Communications

Covers stochastic models for communications, focusing on random variables, Markov chains, Poisson processes, and probability calculations.

Estimating R: Theory of Poisson Processes

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