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The complex nature of inertial confinement fusion (ICF) experiments resultsin a very large number of experimental parameters that are only known withlimited reliability. These parameters, combined with the myriad physical modelsthat govern target evolution, make the reliable extraction of physics fromexperimental campaigns very difficult. We develop an inference method thatallows all important experimental parameters, and previous knowledge, to betaken into account when investigating underlying microphysics models. Theresult is framed as a modified $\chi^{2}$ analysis which is easy to implementin existing analyses, and quite portable. We present a first application to arecent convergent ablator experiment performed at the NIF, and investigate theeffect of variations in all physical dimensions of the target (very difficultto do using other methods). We show that for well characterised targets inwhich dimensions vary at the 0.5% level there is little effect, but 3%variations change the results of inferences dramatically. Our Bayesian methodallows particular inference results to be associated with prior errors inmicrophysics models; in our example, tuning the carbon opacity to matchexperimental data (i.e., ignoring prior knowledge) is equivalent to an assumedprior error of 400% in the tabop opacity tables. This large error isunreasonable, underlining the importance of including prior knowledge in theanalysis of these experiments.

Development of a Bayesian method for the analysis of inertial confinement fusion experiments on the NIF