Designing and testing inflationary models with Bayesian networks
  Even simple inflationary scenarios have many free parameters. Beyond the
variables appearing in the inflationary action, these include dynamical initial
conditions, the number of fields, and couplings to other sectors. These
quantities are often ignored but cosmological observables can depend on the
unknown parameters. We use Bayesian networks to account for a large set of
inflationary parameters, deriving generative models for the primordial spectra
that are conditioned on a hierarchical set of prior probabilities describing
the initial conditions, reheating physics, and other free parameters. We use
$N_f$--quadratic inflation as an illustrative example, finding that the number
of $e$-folds $N_*$ between horizon exit for the pivot scale and the end of
inflation is typically the most important parameter, even when the number of
fields, their masses and initial conditions are unknown, along with possible
conditional dependencies between these parameters.
