Paper Info
Title
Learning the DAG of bayesian belief networks by asking (conditional) (in-)dependence questions
Abstract
Bayesian belief networks (BBNs) have become the de facto standard for the representation of uncertain knowledge. They consist of a qualitative and of a quantitative part describing the (in-)dependencies between the variables of interest as a directed acyclic graph (DAG) and the decomposition of the joint probability distribution (JPD) as a product of conditional probability distributions constrained by the structure of the DAG. In this paper we present a new constraint-based query procedure: Query-an-Oracle (QAO). We assume that an oracle -- preferable a human domain expert -- is at hand which is competent and willing to answer questions generated by QAO concerning the directed (causal) dependence and (conditional) independence of the relevant random variables in the domain. Compared to other structure learning methods (e.g. the PC-Algorithm of Peter Spirtes and Clark Glymour and the IC-Algorithm of Pearl) QAO has a number of advantages. It derives the DAG of the BBN with less computational complexity, with no redundant questions, and is able to exploit directed dependence information without urging oracles to differentiate between direct and indirect influence.
Year
DOI
Venue
2009
10.1145/1597735.1597781
K-CAP
Keywords
Field
DocType
indirect influence,peter spirtes,acyclic graph,joint probability distribution,conditional probability distribution,clark glymour,dependence question,bayesian belief network,computational complexity,dependence information,human domain expert,psychology,conditional independence,directed acyclic graph,probability distribution,conditional probability,random variable,bayesian network,measurement,design
Random variable,Joint probability distribution,Conditional probability,Computer science,Oracle,Directed acyclic graph,Bayesian network,Artificial intelligence,Graphical model,Chain rule (probability)
Conference
Citations 
PageRank 
References 
0
0.34
3
Authors
2
Name
Order
Citations
PageRank
Claus Möbus15815.18
Hilke Garbe2214.69