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The qCSF has been used to assess spatial vision in normal and clinical populations. Because it uses the log parabola CSF model, the estimated contrast sensitivities (CSs) across different spatial frequencies (SFs) are not independent. Most studies have used the area under Log CSF (AULCSF) to simplify statistical inference. Here, we developed a hierarchical Bayesian model (HBM) with population, individual and test levels to compute the joint posterior CS distribution across all levels and SFs to enable statistical inference on CS at individual SFs, using covariance hyperparameters at the population and individual levels to capture the relationship between CSs across SFs. We applied the HBM to a dataset of 112 subjects tested with qCSF trials in each of three (L, M & H) luminance conditions (Hou et al., 2016). The HBM recovered correlations between CSs in pairs of 19 SFs (0.739 to 0.948,-0.065 to 0.451, and-0 …