The Impact of Variable Ordering on Bayesian Network Structure Learning

arXiv:2206.08952v2 Announce Type: replace
Abstract: Causal Bayesian Networks provide an important tool for reasoning under uncertainty with potential application to many complex causal systems. Structure learning algorithms that can tell us something about the causal structure of these systems are becoming increasingly important. In the literature, the validity of these algorithms is often tested for sensitivity over varying sample sizes, hyper-parameters, and occasionally objective functions. In this paper, we show that the order in which the variables are read from …

abstract algorithms application arxiv bayesian causal cs.lg impact literature network networks reasoning something systems tool type uncertainty