  We implemented symbolic regression techniques to identify suitable analytical
functions that map various properties of neutron stars (NSs), obtained by
solving the Tolman-Oppenheimer-Volkoff (TOV) equations, to a few key parameters
of the equation of state (EoS). These symbolic regression models (SRMs) are
then employed to perform Bayesian inference with a comprehensive dataset from
nuclear physics experiments and astrophysical observations. The posterior
distributions of EoS parameters obtained from Bayesian inference using SRMs
closely match those obtained directly from the solutions of TOV equations. Our
SRM-based approach is approximately 100 times faster, enabling efficient
Bayesian analyses across different combinations of data to explore their
sensitivity to various EoS parameters within a reasonably short time.
