Bayesian structure learning and sampling of Bayesian networks with the R package BiDAG

TL;DR

The BiDAG R package offers advanced MCMC methods for efficient structure learning and sampling of Bayesian networks, including a hybrid approach for large graphs and support for dynamic models.

Contribution

Introduction of a hybrid structure learning method combining reduced search space with iterative MCMC for large Bayesian networks.

Findings

Enables MAP graph inference using reduced search space.

Provides efficient posterior sampling with order and partition MCMC.

Supports both static and dynamic Bayesian networks.

Abstract

The R package BiDAG implements Markov chain Monte Carlo (MCMC) methods for structure learning and sampling of Bayesian networks. The package includes tools to search for a maximum a posteriori (MAP) graph and to sample graphs from the posterior distribution given the data. A new hybrid approach to structure learning enables inference in large graphs. In the first step, we define a reduced search space by means of the PC algorithm or based on prior knowledge. In the second step, an iterative order MCMC scheme proceeds to optimize within the restricted search space and estimate the MAP graph. Sampling from the posterior distribution is implemented using either order or partition MCMC. The models and algorithms can handle both discrete and continuous data. The BiDAG package also provides an implementation of MCMC schemes for structure learning and sampling of dynamic Bayesian networks.