Learning Mixtures of Gaussians
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Assessing sensor reliability for multisensor data fusion within the transferable belief model
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Modeling of reliability with possibility theory
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
The Knowledge Engineering Review
A characteristic function approach to inconsistency measures for knowledge bases
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
Evidential fusion for gender profiling
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
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In medical clinical trials, overall trial results are highlighted in the abstractsof papers/reports. These results are summaries of underlying statistical analysis where most of the time normal distributions are assumed in the analysis. It is common for clinicians to focus on the information in the abstracts in order to review or integrate several clinical trial results that address the same or similar medical question(s). Therefore, developing techniques to merge results from clinical trials based on information in the abstracts is useful and important. In reality information in an abstract can either provide sufficient details about a normal distribution or just partial information about a distribution. In this paper, we first propose approaches to constructing normal distributions from both complete and incomplete statistical information in the abstracts. We then provide methods to merge these normal distributions (or sampling distributions). Following this, we investigate the conditions under which two normal distributions can be merged. Finally, we design an algorithm to sequence the merging of trials results to ensure that the most reliable trials are considered first.