7f53f8c6c730af6aeb52e66eb74d8507-Reviews.html

This paper considers learning to sample from the posterior distribution of a model, by directly predicting latent variables from data. The idea is tested in the block MCMC context, where a small block of latents are predicted from the current state of other latents (and the data). This is shown to perform better than single-site Gibbs when variables are highly correlated and there is sufficient data to train the predictors. The paper is well written and has a reasonable evaluation. The comparison between block MCMC and single-site Gibbs is unsurprising.