ProDAG: Projection-induced variational inference for directed acyclic graphs

arXiv:2405.15167v1 Announce Type: cross
Abstract: Directed acyclic graph (DAG) learning is a rapidly expanding field of research. Though the field has witnessed remarkable advances over the past few years, it remains statistically and computationally challenging to learn a single (point estimate) DAG from data, let alone provide uncertainty quantification. Our article addresses the difficult task of quantifying graph uncertainty by developing a variational Bayes inference framework based on novel distributions that have support directly on the space of DAGs. The …

abstract advances article arxiv cs.lg dag data graph graphs inference learn projection quantification research stat.ml type uncertainty