Is a single unique Bayesian network enough to accurately represent your data?

TL;DR

This paper introduces mcmcabn, an R package implementing a flexible MCMC-based approach to Bayesian network structure learning, addressing overfitting concerns in complex systems with limited data.

Contribution

It presents a novel, accessible MCMC method for Bayesian network structure learning that captures multiple supported network structures instead of relying on a single model.

Findings

Provides an R implementation of the MC3 algorithm

Enables exploration of network structure landscape

Addresses overfitting in complex system modeling

Abstract

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 major practical concern is avoiding overfitting, on account of its extreme flexibility and its modelling richness. Many approaches have been proposed to control for overfitting. Unfortunately, they essentially all rely on very crude decisions that result in too simplistic approaches for such complex systems. In practice, with limited data sampled from complex system, this approach seems too simplistic. An alternative would be to use the Monte Carlo Markov chain model choice (MC3) over the network to learn the landscape of reasonably supported networks, and then to present all possible arcs with their MCMC support. This paper presents an R implementation, called mcmcabn, of a flexible structural MC3 that is accessible to non-specialists.