Optimal elicitation of probabilistic information from experts
Published:January 01, 2004
Andrew Curtis, Rachel Wood, 2004. "Optimal elicitation of probabilistic information from experts", Geological Prior Information: Informing Science and Engineering, Andrew Curtis, Rachel Wood
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It is often desirable to describe information derived from the cumulative experience of human experts in a quantitative and probabilistic form. Pertinent examples include assessing the reliability of alternative models or methods of data analysis, estimating the reliability of data in cases where this cannot be measured, and estimating ranges and probable distributions of rock properties and architectures in complex geological settings.
This paper presents a method to design an optimized process of elicitation (interrogation of experts for information) in real time, using all available information elicited previously to help in designing future elicitation trials. The method maximizes expected information during each trial using experimental design theory. We demonstrate this method in a simple experiment in which the conditional probability distribution or relative likelihood of a suite of nine possible 3-D models of fluvial-deltaic geologies was elicited from a geographically remote expert. Although a geological example is used, the method is general and can be applied in any situation in which estimates of expected probabilities of occurrence of a set of discrete models are desired.
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Geological Prior Information: Informing Science and Engineering
Geological prior information represents a new and emerging field within the geosciences. Prior information is the term used to describe previously existing knowledge that can be brought to bear on a new problem. This volume describes a range of methods that can be used to find solutions to practical and theoretical problems using geological prior information, and the nature of geological information that can be so employed. As such, this volume defines how geology can be influential far beyond the confines of its own definition.