Interplay between Swampland and Bayesian Machine Learning in
  constraining cosmological models
  Constraints on a dark energy dominated Universe are obtained from an
interplay between Bayesian Machine Learning and string Swampland criteria. The
approach here differs from previous studies, since in the generative process
Swampland criteria are used and, only later, the results of the fit are
validated, by using observational data-sets. A generative process based
Bayesian Learning approach is applied to two models and the results are
validated by means of available $H(z)$ data. For the first model, a
parametrization of the Hubble constant is considered and, for the second, a
parametrization of the deceleration parameter. This study is motivated by a
recent work, where constraints on string Swampland criteria have been obtained
from a Gaussian Process and $H(z)$ data. However, the results obtained here are
fully independent of the observational data and allow to estimate how the
high-redshift behavior of the Universe will affect the low-redshift one.
Moreover, both parameterizations in the generative process, for the Hubble and
for the deceleration parameters, are independent of the dark energy model. The
outcome, both data- and dark energy model-independent, may highlight, in the
future, the borders of the Swampland for the low-redshift Universe and help to
develop new string-theory motivated dark-energy models. The string Swampland
criteria considered might be in tension with recent observations indicating
that phantom dark energy cannot be in the Swampland. Finally, a spontaneous
sign switch in the dark energy equation of state parameter is observed when the
field traverses are in the $z\in[0,5]$ redshift range, a remarkable phenomenon
requiring further analysis.
