  In a companion paper, we presented BayesCal, a mathematical formalism for
mitigating sky-model incompleteness in interferometric calibration. In this
paper, we demonstrate the use of BayesCal to calibrate the degenerate gain
parameters of full-Stokes simulated observations with a HERA-like hexagonal
close-packed redundant array, for three assumed levels of completeness of the a
priori known component of the calibration sky model. We compare the BayesCal
calibration solutions to those recovered by calibrating the degenerate gain
parameters with only the a priori known component of the calibration sky model
both with and without imposing physically motivated priors on the gain
amplitude solutions and for two choices of baseline length range over which to
calibrate. We find that BayesCal provides calibration solutions with up to four
orders of magnitude lower power in spurious gain amplitude fluctuations than
the calibration solutions derived for the same data set with the alternate
approaches, and between $\sim10^7$ and $\sim10^{10}$ times smaller than in the
mean degenerate gain amplitude on the full range of spectral scales accessible
in the data. Additionally, we find that in the scenarios modelled only BayesCal
has sufficiently high fidelity calibration solutions for unbiased recovery of
the 21 cm power spectrum on large spectral scales ($k_\parallel \lesssim
0.15~h\mathrm{Mpc}^{-1}$). In all other cases, in the completeness regimes
studied, those scales are contaminated.
