Abstract

AbstractNumerical techniques for the computation of posterior distribution ease restrictions on inference models, and multiple plasma parameters can be simultaneously inferred with detailed modelling of uncertainties. In addition, data that depend on multiple parameters in a complex manner can also be utilized. This paper discusses frameworks that further expand the inference capability by including transport properties in plasmas. A prior belief based on a transport equation helps to localize the posterior distribution, and when ample diagnostic capability is available, even unknown parameters in the transport model can be estimated. Furthermore, these transport parameters can be directly inferred when the profiles of the measurable plasma parameters can be easily calculated for the given transport properties.

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