11 December 2015
Montreal
Etc/GMT-5 timezone

An alternative to ABC for likelihood-free inference

11 Dec 2015, 16:30
40m
Montreal

Montreal

Speaker

Kyle Stuart Cranmer (New York University (US))

Description

The field of particle physics has the luxury of very predictive models of the data based on quantum field theory; however, the simulation of a complicated experimental apparatus makes it impractical to directly evaluate the likelihood for a given observation. A popular approach to this class of problems is Approximate Bayesian Computation (ABC). I will describe an alternative technique for parameter inference in this “likelihood-free” setting that is based on a parametrized family of classifiers and univariate density estimation. I will end with examples where this technique is being applied to problems at the LHC.

Presentation Materials