  In this paper, we use The Quijote simulations in order to extract the
cosmological parameters through Bayesian Neural Networks. This kind of model
has a remarkable ability to estimate the associated uncertainty, which is one
of the ultimate goals in the precision cosmology era. We demonstrate the
advantages of BNNs for extracting more complex output distributions and
non-Gaussianities information from the simulations.
