Neural network approximations to posterior densities: an analytical approach

Lennart Hoogerheide, Johan Kaashoek and Herman van Dijk

No EI 2003-38, Econometric Institute Research Papers from Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute

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.

Keywords: Bayesian inference; Markov chain Monte Carlo; importance sampling; neural networks (search for similar items in EconPapers)
Date: 2003-08-07
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Citations: View citations in EconPapers (2)

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