Template-Type: ReDIF-Paper 1.0
Author-Name: Hoogerheide, L.F.
Author-Name-Last: Hoogerheide
Author-Name-First: Lennart
Author-Name: Kaashoek, J.F.
Author-Name-Last: Kaashoek
Author-Name-First: Johan
Author-Name: van Dijk, H.K.
Author-Name-Last: van Dijk
Author-Name-First: Herman
Author-Person: pva325
Title: Neural network approximations to posterior densities: an analytical approach
Abstract: In Hoogerheide, Kaashoek and Van Dijk (2002) the class of neural network sampling methods is introduced to sample from a target (posterior) distribution that may be multi-modal or skew, or exhibit strong correlation among the parameters. In these methods the neural network is used as an importance function in IS or as a candidate density in MH. In this note we suggest an analytical approach to estimate the moments of a certain (target) distribution, where `analytical' refers to the fact that no sampling algorithm like MH or IS is needed.We show an example in which our analytical approach is feasible, even in a case where a `standard' Gibbs approach would fail or be extremely slow.
Creation-Date: 2003-08-07
File-URL: https://repub.eur.nl/pub/1047/ei200338.pdf
File-Format: application/pdf
Series: RePEc:ems:eureir
Number: EI 2003-38
Keywords: Bayesian inference, Markov chain Monte Carlo, importance sampling, neural networks
Handle: RePEc:ems:eureir:1047