EndUser 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 userbased architecture that enables end users to create and to manipulate the knowledge representation. We use the problem of physicians' interpretation of twoarm 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 domainspecific, userbased semantic interface, called a patientflow diagram, in the TAPRCT problem. Pennitted transformations of the statistical model that maintain the semantic relationships of the model are encoded in a metadatastate diagram, called the cohortstate 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: 4854
PS Link:
PDF Link: /papers/93/p48lehmann.pdf
BibTex:
@INPROCEEDINGS{Lehmann93,
AUTHOR = "Harold Lehmann
and Ross Shachter",
TITLE = "EndUser Construction of Influence Diagrams for Bayesian Statistics",
BOOKTITLE = "Proceedings of the Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI93)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1993",
PAGES = "4854"
}

