  Upcoming experiments such as Hydrogen Epoch of Reionization Array (HERA) and
Square Kilometre Array (SKA) are intended to measure the 21cm signal over a
wide range of redshifts, representing an incredible opportunity in advancing
our understanding about the nature of cosmic Reionization. At the same time
these kind of experiments will present new challenges in processing the
extensive amount of data generated, calling for the development of automated
methods capable of precisely estimating physical parameters and their
uncertainties. In this paper we employ Variational Inference, and in particular
Bayesian Neural Networks, as an alternative to MCMC in 21 cm observations to
report credible estimations for cosmological and astrophysical parameters and
assess the correlations among them.
