Bayesian Inference of Phenomenologycal EoS of Neutron Stars with Recent Observations

The description of stellar interiors remains as a big challenge for the nuclear astrophysics community. The consolidated knowledge is restricted to density regions around the saturation of hadronic matter $\rho _{0} = 2.8\times 10^{14} {\rm\ g\ cm^{-3}}$, regimes where our nuclear models are successfully applied. As one moves towards higher densities and extreme conditions up to five to twenty times $\rho_{0}$, little can be said about the microphysics of such objects. Here, we employ a Markov Chain Monte Carlo (MCMC) strategy in order to access the variability of polytropic three-pircewised models for neutron star equation of states. With a fixed description of the hadronic matter, we explore a variety of models for the high density regimes leading to stellar masses up to 2.5 $M_{\odot}$. In addition, we also discuss the use of a Bayesian power regression model with heteroscedastic error. The set of EoS from LIGO was used as inputs and treated as data set for testing case.