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First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper describes a Bayesian hierarchical model model for handling mixed-type missing data (i.e., datasets that involve both continuous and discrete data) in large databases. The model relies on the use of latent Gaussian variables whose correlation is modeled using a bilinear latent factor model. Uncertainty on the number of latent factors is accounted for using an Indian Buffet process prior on the factor indicators. General comments: 1) Although the paper does not discuss the issue explicitly, their model treats the missingness mechanism (which determines the probability that a given value is missing) as ignorable. This is unlikely to be the case in most of the databases considered in the illustration, which is a well known to be a serious issue (a classic reference is Rubin 1976, but there is an extensive statistics literature on the topic over the last 40 years).