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<p><b>Abstract</b>—The construction of a large, complex belief network model, like any major system development effort, requires a structured process to manage system design and development. This paper describes a belief network engineering process based on the spiral system lifecycle model. The problem of specifying numerical probability distributions for random variables in a belief network is best treated not in isolation, but within the broader context of the system development effort as a whole. Because structural assumptions determine which numerical probabilities or parameter values need to be specified, there is an interaction between specification of structure and parameters. Evaluation of successive prototypes serves to refine system requirements, ensure that modeling and elicitation effort are focused productively, and prioritize directions of enhancement and improvement for future prototypes. Explicit representation of semantic information associated with probability assessments facilitates tracing of the rationale for modeling decisions, as well as supporting maintenance and enhancement of the knowledge base. </p>
Belief networks, knowledge representation, elicitation, systems engineering, rapid prototyping, agile modeling.

K. B. Laskey and S. M. Mahoney, "Network Engineering for Agile Belief Network Models," in IEEE Transactions on Knowledge & Data Engineering, vol. 12, no. , pp. 487-498, 2000.
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