  Fiducial inference, as generalized by Hannig et al. (2016), is applied to
nonparametric g-modeling (Efron, 2016) in the discrete case. We propose a
computationally efficient algorithm to sample from the fiducial distribution,
and use the generated samples to construct point estimates and confidence
intervals. We study the theoretical properties of the fiducial distribution and
perform extensive simulations in various scenarios. The proposed approach
yields good statistical performance in terms of the mean squared error of point
estimators and the coverage of confidence intervals. Furthermore, we apply the
proposed fiducial method to estimate the probability of each satellite site
being malignant using gastric adenocarcinoma data with 844 patients (Efron,
2016).
