Template-Type: ReDIF-Paper 1.0
Author-Name: Hoogerheide, L.F.
Author-Name-Last: Hoogerheide
Author-Name-First: Lennart
Author-Name: van Dijk, H.K.
Author-Name-Last: van Dijk
Author-Name-First: Herman
Author-Person: pva325
Title: Note on neural network sampling for Bayesian inference of mixture processes
Abstract: 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.
Creation-Date: 2007-04-30
File-URL: https://repub.eur.nl/pub/10090/EI200715.pdf
File-Format: application/pdf
Series: RePEc:ems:eureir
Number: EI 2007-15
Handle: RePEc:ems:eureir:10090