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In this study general methods for assessing and optimising the measuring capabilities of complex diagnostic systems are developed. In general Monte-Carlo techniques are required for modelling the uncertainties in estimates, significantly limiting the scope of investigations to assess and optimize diagnostic capabilities. In the limit where uncertainties in data and nuisance parameters can be represented by sets of normally distributed stochastic variables, recently developed maximum likelihood estimators provide readily evaluated analytic expressions for the uncertainties in the estimated parameters as functions of the uncertainties in both data and nuisance parameters. These expressions are developed into a comprehensive set of methods for analysing diagnostic capabilities and gaining insight into the functional relationship between diagnostic capability and uncertainties in nuisance parameters and data.

Additional information

Authors: BINDSLEV H, JET Joint Undertaking, Abingdon, Oxon (GB)
Bibliographic Reference: Report: JET-R(97)12 EN (1997) 33pp.
Availability: Available from the Publications Officer, JET Joint Undertaking, Abingdon, Oxon, OX14 3EA (GB)
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