  Bayesian parameter inference techniques require a choice of prior
distribution which can strongly impact the statistical conclusions drawn. We
discuss the construction of least-informative priors for neutrinoless double
beta decay searches. Such priors attempt to be objective by maximizing the
information gain from an experimental setup. In a parametrization using the
lightest neutrino mass $m_l$ and an effective Majorana phase parameter $\Phi$,
we construct such a prior using two different approaches and compare them with
the standard flat and logarithmic priors in $m_l$.
