We describe a Bayesian approach for learning Bayesian networks from a combination of prior knowledge and statistical data that makes use of a prior network.Expand

In this paper we prove the so-called "Meek Conjecture". In particular, we show that if a DAG H is an independence map of another DAG G, then there exists a finite sequence of edge additions and… Expand

We describe a convenient graphical representation for an equivalence class of structures, and introduce a set of operators that can be applied to that representation by a search algorithm to move among equivalence classes.Expand

We show that the search problem of identifying a Bayesian network—among those where each node has at most K parents—that has a relative posterior probability greater than a given constant is NP-complete, when the BDe metric is used.Expand

This work shows how to leverage causal inference to understand the behavior of complex learning systems interacting with their environment and predict the consequences of changes to the system.Expand

We apply a Bayesian approach to learning Bayesian networks that contain decision-graphs| generalizations of decision trees that can encode arbitrary equality constraints.Expand

We discuss Bayesian methods for model averaging and model selection among Bayesian-network models with hidden variables in which the root node is hidden.Expand