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First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. Summary: The paper introduces an iterative extension of NADE (Neural autoregressive distribution estimator), a generative model that uses a neural network with a variable number of inputs to model each conditional in an autoregressive factorization of a joint distribution. The paper builds up on top of an order-agnostic version of NADE where all dimensions not present in the input are modelled independently by the network at each autoregressive step. The main idea introduced in the paper is using a prediction of the missing inputs at each iteration, starting with the marginal probability distribution over the training data, and the factorial (each dimension is predicted independently conditioned on the input) approximation obtained from NADE in the following iterations. The authors hypothesise that prediction in several steps is easier than in one step.