Sciweavers

Share
UAI
1996

Learning Equivalence Classes of Bayesian Network Structures

11 years 2 months ago
Learning Equivalence Classes of Bayesian Network Structures
Two Bayesian-network structures are said to be equivalent if the set of distributions that can be represented with one of those structures is identical to the set of distributions that can be represented with the other. Many scoring criteria that are used to learn Bayesiannetwork structures from data are score equivalent; that is, these criteria do not distinguish among networks that are equivalent. In this paper, we consider using a score equivalent criterion in conjunction with a heuristic search algorithm to perform model selection or model averaging. We argue that it is often appropriate to search among equivalence classes of network structures as opposed to the more common approach of searching among individual Bayesian-network structures. 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. We show that our equi...
David Maxwell Chickering
Added 02 Nov 2010
Updated 02 Nov 2010
Type Conference
Year 1996
Where UAI
Authors David Maxwell Chickering
Comments (0)
books