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Poisson Processes: Applications in Machine Learning

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If you have a question about this talk, please contact Konstantina Palla.

Poisson processes form an incredibly powerful class of distributions with elegant modelling properties. Whilst thoroughly studied in the applied probability community over the last few decades, they have yet to stir up a big fuss in the machine learning community. Nonetheless, there have been a collection of fairly recent papers which apply them in a variety of interesting ways.

In this talk I aim to outline some basic definitions and properties of Poisson processes and give a flavour of how they can be used, incorporating some Bayesian nonparametric machinery. To benefit most from the talk, it’d be helpful to familiarise yourself with the definition and basic properties of a Poisson process.

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

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