map-equal-priors-reduces-to-mle

Summary

When the system has no prior preference for any hypothesis, the more complex Bayesian best-guess (weighing data plus expectations) collapses into the simpler approach of just picking the hypothesis that best explains the observed data. This means the system can skip Bayesian bookkeeping and fall back on plain data-fitting whenever it genuinely has no background assumptions to incorporate, saving computation and avoiding false precision from fabricated priors.