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
Six (or so) things you can do with a bad model
Operations Research
Local expression languages for probabilistic dependence: a preliminary report
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Conflict and surprise: heuristics for model revision
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
A sensitivity analysis of pathfinder: a follow-up study
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Probabilistic similarity networks
Probabilistic similarity networks
Evaluating decision support and expert systems
Evaluating decision support and expert systems
Systems engineering
Explanation in Bayesian belief networks
Explanation in Bayesian belief networks
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Analysis in HUGIN of data conflict
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Dynamic construction of belief networks
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Representing and combining partially specified CPTs
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Constructing situation specific belief networks
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Abstraction in belief networks: the role of intermediate states in diagnostic reasoning
UAI'95 Proceedings of the Eleventh 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
Why is diagnosis using belief networks insensitive to imprecision in probabilities?
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Parameterisation and evaluation of a Bayesian network for use in an ecological risk assessment
Environmental Modelling & Software
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
Design of an intrusion detection system based on Bayesian networks
WSEAS Transactions on Computers
A modular design of Bayesian networks using expert knowledge: Context-aware home service robot
Expert Systems with Applications: An International Journal
Parameterising bayesian networks
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
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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.