Bayesian Structure Learning and Sampling of Bayesian Networks with the R Package BiDAG

Bayesian Structure Learning and Sampling of Bayesian Networks with the R Package BiDAG

Bayesian networks, dynamic Bayesian networks, structure learning, Bayesian inference, MCMC, R

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 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.

Polina Suter

https://www.jstatsoft.org/article/view/v105i09

ETH Zürich

January 2023

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