Bayesian Structure Learning with Generative Flow Networks. (arXiv:2202.13903v2 [cs.LG] UPDATED)

In Bayesian structure learning, we are interested in inferring a distribution
over the directed acyclic graph (DAG) structure of Bayesian networks, from
data. Defining such a distribution is very challenging, due to the
combinatorially large sample space, and approximations based on MCMC are often
required. Recently, a novel class of probabilistic models, called Generative
Flow Networks (GFlowNets), have been introduced as a general framework for
generative modeling of discrete and composite objects, such as graphs. In this
work, we propose …

arxiv bayesian flow learning lg networks