  The interior of a neutron star is expected to exhibit different states of
matter. In particular, complex non-spherical configurations known as `pasta'
phases may exist at the highest densities in the inner crust, potentially
having an impact on different neutron-star phenomena. We study the properties
of the pasta phase and the uncertainties in the pasta observables which are due
to our incomplete knowledge of the nuclear energy functional. To this aim, we
employed a compressible liquid-drop model approach with surface parameters
optimised either on experimental nuclear masses or theoretical calculations. To
assess the model uncertainties, we performed a Bayesian analysis by largely
varying the model parameters using uniform priors, and generating posterior
distributions with filters accounting for both our present low-density nuclear
physics knowledge and high-density neutron-star physics constraints. Our
results show that the nuclear physics constraints, such as the neutron-matter
equation of state at very low density and the experimental mass measurements,
are crucial in determining the crustal and pasta observables. Accounting for
all constraints, we demonstrate that the presence of pasta phases is robustly
predicted in an important fraction of the inner crust. We estimate the relative
crustal thickness associated with pasta phases as $R_{\rm pasta}/R_{\rm
crust}=0.128\pm 0.047$ and the relative moment of inertia as $I_{\rm
pasta}/I_{\rm crust}=0.480\pm 0.137$. Our findings indicate that the surface
and curvature parameters are more influential than the bulk parameters for the
description of pasta observables. We also show that using a surface tension
that is inconsistent with the bulk functional leads to an underestimation of
both the average values and the uncertainties in the pasta properties, thus
highlighting the importance of a consistent calculation of the nuclear
functional.
