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

A Bayesian network is a probabilistic graphical model, which represents conditional independence relationships between a set of random variables by a directed acyclic graph (DAG).The problem of DAG learning from observational data is hard (Chickering 1996), and the number of DAGs grows super-exponentially with the number of nodes. Hence, developing and implementing methods to learn an underlying DAG from observational data in reasonable time continues to be the focus of much research (Bartlett and Cussens 2017; Goudie and Mukherjee 2016; Scanagatta, de Campos, and Corani 2015). Drton and Maathuis (2017) provide an overview of the approaches for structure learning of graphical models including Bayesian networks. The R (R Development Core Team 2008) packages pcalg (Kalisch, Mächler, Colombo, Maathuis, and Bühlmann 2012), BNlearn (Scutari 2010), bnstruct (Franzin, Sambo, and Camillo 2017) and the Java-based toolbox TETRAD (Glymour, Scheines, Spirtes, and Ramsey 2017) implement multiple approaches to structure learning, including both constraint-based and searcharXiv:2105.00488v1