Poisson Process Mapping

Related lectures (30)

Page 1 of 3

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

Covers the theorems of superposition and colouring for Poisson processes.

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

Covers the normal distribution, including its properties and calculations.

Explores mixture models in discrete choice and random parameters estimation results.

Covers the basics of the normal distribution and its applications in probability calculations.

Covers the characteristics and importance of the normal distribution, including examples and treatment scenarios.

Introduces inferential statistics, covering sampling, central tendency, dispersion, histograms, z-scores, and the normal distribution.

Explores the Central Limit Theorem and its applications in statistical analysis.

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