  Gravitational-wave observations have the capability to strongly differentiate
between different assumptions for how binary compact objects form. In this
work, we show how to carefully interpolate a marginal likelihood between
choices of binary evolution model parameters. Using the StarTrack binary
evolution code, we compare one- and four-dimensional binary evolution models to
the compact binary mergers reported in recent gravitational-wave observing
runs. We first consider a one-dimensional model, studying the effect of
supernova kick velocity (drawn from a Maxwellian with dispersion {\sigma}_eff)
on the simulated population of compact binary mergers, and find support for
substantial SN recoil kicks. We follow this up with a four-dimensional study of
{\sigma}_eff, mass transfer efficiency (fa) and the efficiency of angular
momentum depletion from ejected material (\b{eta}) during Roche-lobe accretion,
and an observation-driven reduction in the mass-loss rate estimated from
stellar wind models (fwind1). We find that three of them ({\sigma}_eff, fa, and
fwind1) can be efficiently limited by these observational comparisons. After
initially sampling from a uniform prior in the space of these parameters, we
refined our sampling by iteratively estimating a Bayesian likelihood for each
simulation and fitting that likelihood to a parametric model (a truncated
Gaussian) in order to propose new. Our maximum likelihood simulation (K0559)
has parameters: {\sigma}_eff = 108.3 km/s (indicating substantial SN recoil
kicks), fa = 0.922 (indicating efficient mass transfer), and fwind1 = 0.328
(indicating support for reduced wind-driven mass loss). Note that our estimates
are only valid within one particular model of compact binary formation through
isolated binary evolution and do not yet take into account the impact of other
uncertain pieces of stellar physics and binary evolution.
