Repository for the course project of Bayesian Statistics and Probabilistic Programming. The purpose is to solve the particle physics classification problem proposed in https://www.kaggle.com/c/cernsignal/overview.

Authors: Adrián Fernández Cid, Aitor Lucas Castellano, Marcos Moreno Blanco & Noel Rabella Gras

In this folder you will find:

main.pdf, where we summarise our work, focusing on novel aspects such as the undersampling and the Spike and Slab implementation.

CERN-RandomSamples-Logistic-JAGS.ipynb, a notebook with the vanilla and Horseshoe implementations of a logistic regression with random undersampling in JAGS.

CERN-MinTargSamples-Logistic-JAGS.ipynb, where we replace the above random undersampling by a one-step targetted removal (OSS or NCR) followed by random undersampling to reach an affordable dataset size.

CERN-OssSamples-Logistic-JAGS.ipynb, where we do the same but with our RTR method, implemented with OSS.

CERN-NcrSamples-Logistic-JAGS.ipynb, where we implement the RTR with the NCR instead.

CERN-RandomSamples-Logistic-STAN.ipynb, a notebook with the vanilla and Horseshoe implementations of a logistic regression with random undersampling in STAN.

CERN-OssSamples-Logistic-STAN.ipynb, where we do the same but with our RTR method, implemented with OSS.

CERN-RandomSamples-Logistic-SpikeSlab.ipynb, a notebook with the Spike and Slab implementation of a logistic regression with random undersampling using BoomSpikeSlab.

literature/, a folder with the references we could find and consult.

data/, containing the necessary data for running the notebooks.

We have tried for the documents to be as self-contained as possible while avoiding too much repetition, so some are more verbose than others and there is a preferred ordering: we recommend going through them as they appear above.