In this paper we show some further experiments with neural network sampling, a class of sampling methods that make use of neural network approximations to (posterior) densities, introduced by Hoogerheide et al. (2007). We consider a method where a mixture of Student's t densities, which can be interpreted as a neural network function, is used as a candidate density in importance sampling or the Metropolis-Hastings algorithm. It is applied to an illustrative 2-regime mixture model for the US real GNP growth rate. We explain the non-elliptical shapes of the posterior distribution, and show that the proposed method outperforms Gibbs sampling with data augmentation and the griddy Gibbs sampler.

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Hoogerheide, L.F., & van Dijk, H.K.. (2007). Note on neural network sampling for Bayesian inference of mixture processes (No. EI 2007-15). Report / Econometric Institute, Erasmus University Rotterdam. Retrieved from