Bayesian non parametric modelling of Higgs pair production
1 Department of Statistical Sciences, University of Padova, Via Cesare Battisti 241 - 35121 Padova, Italy
2 INFN, Padova
a e-mail: firstname.lastname@example.org
Published online: 22 March 2017
Statistical classification models are commonly used to separate a signal from a background. In this talk we face the problem of isolating the signal of Higgs pair production using the decay channel in which each boson decays into a pair of b-quarks. Typically in this context non parametric methods are used, such as Random Forests or different types of boosting tools. We remain in the same non-parametric framework, but we propose to face the problem following a Bayesian approach. A Dirichlet process is used as prior for the random effects in a logit model which is fitted by leveraging the Polya-Gamma data augmentation. Refinements of the model include the insertion in the simple model of P-splines to relate explanatory variables with the response and the use of Bayesian trees (BART) to describe the atoms in the Dirichlet process.
© The Authors, published by EDP Sciences, 2017
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