  For addressing the remarkable difference between neutrino and quark mixings,
high-scale mixings relations (HSMR) or unification (HSMU) hypothe\ ses were
proposed. These phenomenology frameworks have been explored with respect to
bounds from neutrino oscillations and relevant cosmologic\ al data. However
there are caveats with regards to assessing the hypotheses' compatibility with
data in a statistically robust and convergent \ manner because most analysis
employ a few sample of points in model parameters' space. A remedy could be
achieved by using Bayesian algorithms\
  for the parameters' space exploration. Using this approach, we made global
fits of the HSMU and HSMR models to data and find compatible param\ eter
regions, including for Majorana phases. The posterior samples could be used for
studying correlations between neutrino observables and pr\ ospects for updates
of related experiments.
