10 citations · 11 across the 2 of their papers we have counts for
5 papers
Additive Bayesian Network Modelling with the R Package abn
Gilles Kratzer, Fraser Iain Lewis, Arianna Comin +2
The R package abn is designed to fit additive Bayesian models to observational datasets. It contains routines to score Bayesian networks based on Bayesian or information theoretic…
Is a single unique Bayesian network enough to accurately represent your data?
Gilles Kratzer, Reinhard Furrer
Bayesian network (BN) modelling is extensively used in systems epidemiology. Usually it consists in selecting and reporting the best-fitting structure conditional to the data. A ma…
Comparison between Suitable Priors for Additive Bayesian Networks
Gilles Kratzer, Reinhard Furrer, Marta Pittavino
Additive Bayesian networks are types of graphical models that extend the usual Bayesian generalized linear model to multiple dependent variables through the factorisation of the jo…
Information-Theoretic Scoring Rules to Learn Additive Bayesian Network Applied to Epidemiology
Gilles Kratzer, Reinhard Furrer
Bayesian network modelling is a well adapted approach to study messy and highly correlated datasets which are very common in, e.g., systems epidemiology. A popular approach to lear…
varrank: an R package for variable ranking based on mutual information with applications to observed systemic datasets
Gilles Kratzer, Reinhard Furrer
This article describes the R package varrank. It has a flexible implementation of heuristic approaches which perform variable ranking based on mutual information. The package is pa…