  In this work, we compare two powerful parameter estimation methods namely
Bayesian inference and Neural Network based learning to study the quark matter
equation of state with constant speed of sound parametrization and the
structure of the quark stars within the two-family scenario. We use the mass
and radius estimations from several X-ray sources and also the mass and tidal
deformability measurements from gravitational wave events to constrain the
parameters of our model. The results found from the two methods are consistent.
The predicted speed of sound is compatible with the conformal limit.
