Gaussian process models are flexible non parametric probabilistic models for functions. In this talk we will show how they can be incorporated into latent variable models to form probabilistic latent variable models. The resulting approaches have some unusual properties. In particular, they express conditional independencies across features, rather than data. This implies that rather than a curse of dimensionality they exhibit a blessing of dimensionality. We will give background of the model and show some exemplar applications.