Bayesian structure learning and sampling of Bayesian networks with the R package BiDAG
This provides a tool for researchers and practitioners in statistics and machine learning to perform Bayesian network analysis, but it is incremental as it builds on existing MCMC methods with a hybrid approach.
The authors tackled the problem of structure learning and sampling in Bayesian networks by developing the R package BiDAG, which implements MCMC methods including a new hybrid approach that enables inference in large graphs by combining the PC algorithm with iterative order MCMC to find the MAP graph and sample from the posterior distribution.
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.