Poisson point process

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Related lectures (32)

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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.