Neural network approximations to posterior densities: an analytical approach
2003-08-07
Research Paper
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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
Automatically Extracted Terms
- network
- density
- target
- target density
- distribution
- approach
- moment
- function
- approximation
- xi | i
- sampling
- gibbs sampler
- value
- bayesian
- method
- point
- monte
- estimate
- carlo
- network approach
