Efficient entropy-based detection of change-points in streaming data
Add to your list(s)
Download to your calendar using vCal
If you have a question about this talk, please contact Elena Yudovina.
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.
This talk is included in these lists:
Note that ex-directory lists are not shown.
|