We demonstrate and compare three unsupervised Bayesian latent variable models implemented in Infer.NET [2] for biomedical data modeling of 42 skin and aging phenotypes measured on the 12,000 female twins in the Twins UK study [7]. We address various data modeling problems include high missingness, heterogeneous data, and repeat observations. We compare the proposed models in terms of their performance at predicting disease labels and symptoms from available explanatory variables, concluding that factor analysis type models have the strongest statistical performance in this setting. We show that such models can be combined with regression components for improved interpretability.