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A Myopic Approach to Ordering Nodes for Parameter
Elicitation in Bayesian Belief Networks
Abstract
Building Bayesian belief networks in the absence of data involves the challenging task of eliciting
conditional probabilities from experts to parameterize the model. In this paper, we develop an analytical
method for determining the optimal order for eliciting these probabilities. Our method uses prior
distributions on network parameters and a novel expected proximity criteria, to propose an order that
maximizes information gain per unit elicitation time. We present analytical results when priors are
uniform Dirichlet; for other priors, we find through experiments that the optimal order is strongly
affected by which variables are of primary interest to the analyst. Our results should prove useful to
researchers and practitioners involved in belief network model building and elicitation.
Existing system
Building Bayesian belief networks in the absence of data involves the challenging task of eliciting
conditional probabilities from experts to parameterize the model
Proposed system
we develop an analytical method for determining the optimal order for eliciting these probabilities. Our
method uses prior distributions on network parameters and a novel expected proximity criteria, to
propose an order that maximizes information gain per unit elicitation time. We present analytical results
when priors are uniform Dirichlet; for other priors, we find through experiments that the optimal order
is strongly affected by which variables are of primary interest to the analyst. Our results should prove
useful to researchers and practitioners involved in belief network model building and elicitation.

2. SYSTEM CONFIGURATION:-
HARDWARE CONFIGURATION:-
 Processor - Pentium –IV
 Speed - 1.1 Ghz
 RAM - 256 MB(min)
 Hard Disk - 20 GB
 Key Board - Standard Windows Keyboard
 Mouse - Two or Three Button Mouse
 Monitor - SVGA
SOFTWARE CONFIGURATION:-
 Operating System : Windows XP
 Programming Language : JAVA
 Java Version : JDK 1.6 & above.