Software system testing and quality assurance
Software system testing and quality assurance
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Object oriented design with applications
Object oriented design with applications
Advances in probabilistic reasoning
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Evaluating decision support and expert systems
Evaluating decision support and expert systems
Systems engineering
Explanation in Bayesian belief networks
Explanation in Bayesian belief networks
Exploring localization in Bayesian networks for large expert systems
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Utility-based categorization
Analysis in HUGIN of data conflict
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
A Probabilistic Approach to Language Understanding
A Probabilistic Approach to Language Understanding
Probabilistic similarity networks
Probabilistic similarity networks
Abstraction in belief networks: the role of intermediate states in diagnostic reasoning
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Fusion of domain knowledge with data for structural learning in object oriented domains
The Journal of Machine Learning Research
Building large-scale Bayesian networks
The Knowledge Engineering Review
Agent interface enhancement: making multiagent graphical models accessible
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Ontologies for probabilistic networks: a case study in the oesophageal-cancer domain
The Knowledge Engineering Review
Using Ranked Nodes to Model Qualitative Judgments in Bayesian Networks
IEEE Transactions on Knowledge and Data Engineering
Structured modeling language for automated modeling in causal networks
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
The use of a Bayesian network for web effort estimation
ICWE'07 Proceedings of the 7th international conference on Web engineering
Patterns discovery for efficient structured probabilistic inference
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
Representing and combining partially specified CPTs
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
SPOOK: a system for probabilistic object-oriented knowledge representation
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Causal mechanism-based model constructions
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Object-oriented Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Network fragments: representing knowledge for constructing probabilistic models
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Building an expert-based web effort estimation model using bayesian networks
EASE'09 Proceedings of the 13th international conference on Evaluation and Assessment in Software Engineering
Predicting web development effort using a bayesian network
EASE'07 Proceedings of the 11th international conference on Evaluation and Assessment in Software Engineering
Proceedings of the 34th International Conference on Software Engineering
Structured probabilistic inference
International Journal of Approximate Reasoning
State-of-the-art of intention recognition and its use in decision making
AI Communications
Intelligent Decision Technologies
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Developing a large belief network, like any large system, requires systems engineering to manage the design and construction process. We propose that network engineering follow a rapid prototyping approach to network construction. We describe criteria for identifying network modules and the use of 'stubs' within a belief network. We propose an object oriented representation for belief networks which captures the semantic as well as representational knowledge embedded in the vaziables, their values and their parameters. Methods for evaluating complex networks are described. Throughout the discussion, tools which support the engineering of large belief networks are identified.