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Poisson Processes

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If you have a question about this talk, please contact Shakir Mohamed.

We introduce the Poisson process. First, we intuitively describe the Poisson distribution and Poisson process, linking it to the exponential and binomial distributions, and discussing properties and applications. Some of this material is drawn from ‘Probability and Random Processes’ by Grimmett and Stirzaker.

We also describe recent work by Adams et. al (2009), “Tractable Nonparametric Bayesian Inference in Poisson Processes with Gaussian Process Intensities”, where a Gaussian process is used to model the intensity of an inhomogeneous Poisson process. We then examine the Poisson process more closely, following Kingman’s classic book ‘Poisson processes’, which makes use of measure theory.

This talk is part of the Machine Learning Reading Group @ CUED series.

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