  Asteroseismic observations are crucial to constrain stellar models with
precision. Bayesian Estimation of STellar Parameters (BESTP) is a tool that
utilizes Bayesian statistics and nested sampling Monte Carlo algorithm to
search for the stellar models that best match a given set of classical and
asteroseismic constraints from observations. The computation and evaluation of
models are efficiently performed in an automated and a multi-threaded way. To
illustrate the capabilities of BESTP, we estimate fundamental stellar
properties for the Sun and the red-giant star HD 222076. In both cases, we find
models that are consistent with the observations. We also evaluate the
improvement in the precision of stellar parameters when the oscillation
frequencies of individual modes are included as constraints, compared to the
case when only the the large frequency separation is included. For the solar
case, the uncertainties of estimated masses, radii and ages are reduced by
0.7%, 0.3% and 8% respectively. For HD 222076, they are reduced even more
noticeably by 2%, 0.5% and 4.7%. We also note an improvement of 10% for the age
of HD 222076 when the Gaia parallax is included as a constraint compared to the
case when only the large separation is included as constraint.
