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FLAIRS
2001

A Method for Evaluating Elicitation Schemes for Probabilities

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A Method for Evaluating Elicitation Schemes for Probabilities
We present an objective approach for evaluating probability elicitation methods in probabilistic models. Our method draws on ideas from research on learning Bayesian networks: if we assume that the expert's knowledge is manifested essentially as a database of records that have been collected in the course of the expert's experience, and if this database of records were available to us, then the structure and parameters of the expert's beliefs could be reliably constructed using techniques for Bayesian learning from data. This learned model could, in turn, be compared to elicited models to judge the effectiveness of the elicitation process. We describe a general procedure by which it is possible to capture the data corresponding to the expert's beliefs, and we present a simple experiment in which we utilize this technique to compare three methods for eliciting discrete probabilities: (1) direct numerical assessment, (2) the probability wheel, and (3) the scaled prob...
Haiqin Wang, Denver Dash, Marek J. Druzdzel
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2001
Where FLAIRS
Authors Haiqin Wang, Denver Dash, Marek J. Druzdzel
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