Uncertainty in Artificial Intelligence
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End-User Construction of Influence Diagrams for Bayesian Statistics
Harold Lehmann, Ross Shachter
Abstract:
Influence diagrams are ideal knowledge representations for Bayesian statistical models. However, these diagrams are difficult for end users to interpret and to manipulate. We present a user-based architecture that enables end users to create and to manipulate the knowledge representation. We use the problem of physicians' interpretation of two-arm parallel randomized clinical trials (TAPRCT) to illustrate the architecture and its use. There are three primary data structures. Elements of statistical models are encoded as subgraphs of a restricted class of influence diagram. The interpretations of those elements are mapped into users' language in a domain-specific, user-based semantic interface, called a patient-flow diagram, in the TAPRCT problem. Pennitted transformations of the statistical model that maintain the semantic relationships of the model are encoded in a metadata-state diagram, called the cohort-state diagram, in the TAPRCT problem. The algorithm that runs the system uses modular actions called construction steps. This framework has been implemented in a system called THOMAS, that allows physicians to interpret the data reported from a TAPRCT.
Keywords: statistics, Bayesian, expert systems, artificial intelligence, influence diagrams, pr
Pages: 48-54
PS Link:
PDF Link: /papers/93/p48-lehmann.pdf
BibTex:
@INPROCEEDINGS{Lehmann93,
AUTHOR = "Harold Lehmann and Ross Shachter",
TITLE = "End-User Construction of Influence Diagrams for Bayesian Statistics",
BOOKTITLE = "Proceedings of the Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-93)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1993",
PAGES = "48--54"
}


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