Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider

1 code implementation • 16 May 2021 • Anna Stakia, Tommaso Dorigo, Giovanni Banelli, Daniela Bortoletto, Alessandro Casa, Pablo de Castro, Christophe Delaere, Julien Donini, Livio Finos, Michele Gallinaro, Andrea Giammanco, Alexander Held, Fabricio Jiménez Morales, Grzegorz Kotkowski, Seng Pei Liew, Fabio Maltoni, Giovanna Menardi, Ioanna Papavergou, Alessia Saggio, Bruno Scarpa, Giles C. Strong, Cecilia Tosciri, João Varela, Pietro Vischia, Andreas Weiler

Between the years 2015 and 2019, members of the Horizon 2020-funded Innovative Training Network named "AMVA4NewPhysics" studied the customization and application of advanced multivariate analysis methods and statistical learning tools to high-energy physics problems, as well as developed entirely new ones.

Dynamic modeling of mortality via mixtures of skewed distribution functions

1 code implementation • 2 Feb 2021 • Emanuele Aliverti, Stefano Mazzuco, Bruno Scarpa

There has been growing interest on forecasting mortality.

Applications

Analysis of association football playing styles: an innovative method to cluster networks

no code implementations • 28 May 2018 • Jacopo Diquigiovanni, Bruno Scarpa

In this work we develop an innovative hierarchical clustering method to divide a sample of undirected weighted networks into groups.

Applications

Locally Adaptive Bayesian Multivariate Time Series

no code implementations • NeurIPS 2013 • Daniele Durante, Bruno Scarpa, David B. Dunson

In modeling multivariate time series, it is important to allow time-varying smoothness in the mean and covariance process.

Bayesian Inference Time Series +1

Locally adaptive factor processes for multivariate time series

no code implementations • 7 Oct 2012 • Daniele Durante, Bruno Scarpa, David B. Dunson

In modeling multivariate time series, it is important to allow time-varying smoothness in the mean and covariance process.

Bayesian Inference Gaussian Processes +2