  Due to increased awareness of data protection and corresponding laws many
data, especially involving sensitive personal information, are not publicly
accessible. Accordingly, many data collecting agencies only release aggregated
data, e.g. providing the mean and selected quantiles of population
distributions. Yet, research and scientific understanding, e.g. for medical
diagnostics or policy advice, often relies on data access. To overcome this
tension, we propose a Bayesian method for learning from quantile information.
Being based on order statistics of finite samples our method adequately and
correctly reflects the uncertainty of empirical quantiles. After outlining the
theory, we apply our method to simulated as well as real world examples. In
addition, we provide a python-based package that implements the proposed model.
