  Semi-analytic modeling furnishes an efficient avenue for characterizing the
properties of dark matter halos associated with satellites of Milky Way-like
systems, as it easily accounts for uncertainties arising from halo-to-halo
variance, the orbital disruption of satellites, baryonic feedback, and the
stellar-to-halo mass (SMHM) relation. We use the SatGen semi-analytic satellite
generator -- which incorporates both empirical models of the galaxy-halo
connection in the field as well as analytic prescriptions for the orbital
evolution of these satellites after they enter a host galaxy -- to create large
samples of Milky Way-like systems and their satellites. By selecting satellites
in the sample that match the observed properties of a particular dwarf galaxy,
we can then infer arbitrary properties of the satellite galaxy within the Cold
Dark Matter paradigm. For the Milky Way's classical dwarfs, we provide inferred
values (with associated uncertainties) for the maximum circular velocity
$v_{max}$ and the radius $r_{max}$ at which it occurs, varying over two choices
of feedback model and two prescriptions for the SMHM relation that populate
dark matter halos with physically distinct galaxies. While simple empirical
scaling relations can recover the median inferred value for $v_{max}$ and
$r_{max}$, this approach provides realistic correlated uncertainties and aids
interpretability through variation of the model. For these different models, we
also demonstrate how the internal properties of a satellite's dark matter
profile correlate with its orbit, and we show that it is difficult to reproduce
observations of the Fornax dwarf without strong baryonic feedback. The
technique developed in this work is flexible in its application of
observational data and can leverage arbitrary information about the satellite
galaxies to make inferences about their dark matter halos and population
statistics.
