Title: Complete Likelihood Objective for Latent Variable Models. Abstract: In this work, we propose an alternative to the Marginal Likelihood (MaL) objective for training latent variable models, Complete Latent Likelihood (CoLLike). We analyze the objectives from the perspective of matching joint distributions. We show that MaL corresponds to a particular $KL$ divergence between some target \emph{joint} distribution and the model joint. Furthermore, the properties of the target joint explain such major malfunctions of MaL as uninformative latents (posterior collapse) and high deviation of the aggregated posterior from the prior. In CoLLike approach, we use a sample from the prior to construct a family of target joint distributions, which properties prevent these drawbacks. We utilize the complete likelihood both to choose the target from this family and to learn the model. We confirm our analysis by experiments with expressive low-dimensional latent variable models, which also indicate that it is possible to achieve high accuracy unsupervised classification using CoLLike objective.