Name
Abstract | ||
|---|---|---|
Whereas acausal Bayesian networks represent probabilistic independence, causal Bayesian networks represent causal relationships. In this paper, we examine Bayesian methods for learning both types of networks. Bayesian methods for learning acausal networks are fairly well developed. These methods often employ assumptions to facilitate the construction of priors, including the assumptions of parameter independence, parameter modularity, and likelihood equivalence. We show that although these assumptions also can be appropriate for learning causal networks, we need additional assumptions in order to learn causal networks. We introduce two sufficient assumptions, called mechanism independence and component independence. We show that these new assumptions, when combined with parameter independence, parameter modularity, and likelihood equivalence, allow us to apply methods for learning acausal networks to learn causal networks. |
Year | Venue | Keywords |
|---|---|---|
2013 | UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence | acausal network,bayesian approach,causal bayesian network,causal relationship,component independence,parameter independence,likelihood equivalence,parameter modularity,mechanism independence,bayesian method,causal network,bayesian network |
DocType | Volume | ISBN |
Journal | abs/1302.4958 | 1-55860-385-9 |
Citations | PageRank | References |
49 | 14.30 | 7 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Heckerman | 1 | 6951 | 1419.21 |