  LUCI is an general-purpose spectral line-fitting pipeline which natively
integrates machine learning algorithms to initialize fit functions. LUCI
currently uses point-estimates obtained from a convolutional neural network
(CNN) to inform optimization algorithms; this methodology has shown great
promise by reducing computation time and reducing the chance of falling into a
local minimum using convex optimization methods. In this update to LUCI, we
expand upon the CNN developed in Rhea et al. 2020 so that it outputs Gaussian
posterior distributions of the fit parameters of interest (the velocity and
broadening) rather than simple point-estimates. Moreover, these posteriors are
then used to inform the priors in a Bayesian inference scheme, either emcee or
dynesty. The code is publicly available at https://github.com/crhea93/LUCI.
