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The paper uses an online approximation to MCMC to draw parameters for a Bayesian neural network. The predictive distribution under these samples is then fitted using stochastic approximation. The comparisons are to recent work on approximate Bayesian inference applied to the same models and example problems. The paper does not yet present demonstrate that these methods will push forward any particular application. The paper is a fairly natural extension of existing work.