University of Cambridge > Talks.cam > Optimization and Incentives Seminar > Efficient entropy-based detection of change-points in streaming data

Efficient entropy-based detection of change-points in streaming data

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It is well-known that the entropy of an unknown stationary source can be consistently estimated using an estimator based on the lengths of long repeated sections of text. I will discuss a method for detecting change points in data sources based on similar information theory-based quantities. I will present the results of some simulations and theoretical results based on properties of the typical set which justify how successful this method can be.

Joint work with Oliver Johnson, Dino Sejdinovic, Christophe Andrieu, Ayalvadi Ganesh and Robert Piechocki

This talk is part of the Optimization and Incentives Seminar series.

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