Evaluation of Bayesian networks used for diagnostics

Przytula, K. Wojtek; Dash, Denver; Thompson, Don

doi:10.1109/aero.2003.1234161

Cited by 18 publications

(9 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The program assumes that BN reflects the reality and uses Markov Chain Monte Carlo simulation to analyze the ability of the decision support system based on the BN to recommend correct conclusions [10]. The model evaluation program lists cases when correct conclusion is not likely or is ambiguous, even when all pertinent evidence is obtained.…”

Section: Application: Bn Based Diagnostic Assistant For Diesel Locomomentioning

confidence: 99%

Collaborative Development of Large Bayesian Networks

Przytula

Isdale

2006

2006 IEEE Autotestcon

View full text Add to dashboard Cite

Bayesian networks (BN) have been shown to be a very effective form of models for diagnostic assistants. However, the difficulties creating a BN model for complex domains have been a barrier to their use. We present a methodology and supporting software for rapid and robust development of BN models for diagnostic systems. Our approach uses a layered structure of BN and custom node types.These reduce the complexity of the models without degrading their fidelity. Together with keyword tags and policy based conflict resolution, they make it possible to develop subsystem models that are merged into a single integrated model. Extracting and re-merging the subsystem models allows cyclic development by appropriate domain experts.We have developed an editor for the model that can be used directly by the domain expert without assistance of a knowledge engineer. The expert enters the domain information into simple tables. The BN file used for reasoning and other BN computations is created automatically by the editor.

show abstract

Section: Application: Bn Based Diagnostic Assistant For Diesel Locomomentioning

confidence: 99%

Collaborative Development of Large Bayesian Networks

Przytula

Isdale

2006

2006 IEEE Autotestcon

View full text Add to dashboard Cite

show abstract

“…by executing a mathematical method whose entries are the cases available and the inferential results. Przytula et al (2003) propose an approach that automatically generates its own cases in a way that guarantees a complete evaluation of the model. Their approach uses Monte Carlo simulation to automatically generate diagnostic cases that uniformly cover all the parts of the BN model.…”

Section: Evaluation Methodsmentioning

confidence: 99%

Comparison of Two Types of Event Bayesian Networks: A Case Study

Galán¹,

Figueroa²,

Díez³

et al. 2007

Applied Artificial Intelligence

View full text Add to dashboard Cite

“…The former is that models do not accurately encode the components and observation information of system; the latter is the limitations of diagnostic system, for example, lack of or imperfect sensor system and so on [4][5] [6] . The usual evaluating methods are using special test cases which based on standard cases to test diagnostic models.…”

Section: Analysis About Diagnostic Ability and Means Of Evaluation Fomentioning

confidence: 99%

Research on simulation methods of evaluation for diagnostic Bayesian networks

Lin

2006

SPIE Proceedings

View full text Add to dashboard Cite

Bayesian Networks that based on probability inference is proposed to solve problems of uncertainty and imperfection. It has more advantages to solve faults caused by uncertainty and relevancy of complex devices. Diagnostic models of Bayesian Networks must be evaluated roundly before used for diagnosis. The usual way to evaluate diagnostic models is using standard cases to test the model, but the cases are limited and the quality of these cases depend on their source, and these cases could not include all instances. Based on sample means of Monte Carlo, an algorithm of evaluation for diagnostic models is proposed this article; this algorithm does not need special diagnostic cases. Faults injecting algorithm with equal probability of every components are adopted, test cases are produced by this algorithm of system itself, and course of faults propagation is simulated by this algorithm of system too. This algorithm could test diagnostic models roundly, and make overall evaluation of diagnostic models.

show abstract

Evaluation of Bayesian networks used for diagnostics

Cited by 18 publications

References 9 publications

Collaborative Development of Large Bayesian Networks

Collaborative Development of Large Bayesian Networks

Comparison of Two Types of Event Bayesian Networks: A Case Study

Research on simulation methods of evaluation for diagnostic Bayesian networks

Contact Info

Product

Resources

About