  Noninformative uniform priors are staples of Bayesian inference, especially
in Bayesian machine learning. This study challenges the assumption that they
are optimal and their use in Bayesian inference yields optimal outcomes.
Instead of using arbitrary noninformative uniform priors, we propose a machine
learning based alternative method, learning optimal priors from data by
maximizing a target function of interest. Applying na\"ive Bayes text
classification methodology and a search algorithm developed for this study, our
system learned priors from data using the positive predictive value metric as
the target function. The task was to find Wikipedia articles that had not (but
should have) been categorized under certain Wikipedia categories. We conducted
five sets of experiments using separate Wikipedia categories. While the
baseline models used the popular Bayes-Laplace priors, the study models learned
the optimal priors for each set of experiments separately before using them.
The results showed that the study models consistently outperformed the baseline
models with a wide margin of statistical significance (p < 0.001). The measured
performance improvement of the study model over the baseline was as high as
443% with the mean value of 193% over five Wikipedia categories.
