We describe an approach for increasing the flexibility of an autoregressive model, based on modelling the random numbers that the model uses internally when generating data.Expand

We introduce a simple modification for autoencoder neural networks that yields powerful generative models that are faster than other autoregressive estimators.Expand

We show that Annealed Importance Sampling (AIS) can be used to efficiently estimate the partition function of an RBM, and we present a novel AIS scheme for comparing RBM's with different architectures.Expand

This paper provides a generalization of Moller et al. (2004) and a new MCMC algorithm, which obtains better acceptance probabilities for the same amount of exact sampling, and removes the need to estimate model parameters before sampling begins.Expand

In this paper we present the first approach to Gaussian Cox processes in which it is possible to perform inference without introducing approximations or finitedimensional proxy distributions.Expand

We propose a new approach to likelihood-free inference based on Bayesian conditional density estimation, which can be made as accurate as required.Expand