  We consider the problem of learning parameters of latent variable models from
mixed (continuous and ordinal) data with missing values. We propose a novel
Bayesian Gaussian copula factor (BGCF) approach that is consistent under
certain conditions and that is quite robust to the violations of these
conditions. In simulations, BGCF substantially outperforms two state-of-the-art
alternative approaches. An illustration on the `Holzinger & Swineford 1939'
dataset indicates that BGCF is favorable over the so-called robust maximum
likelihood (MLR) even if the data match the assumptions of MLR.
