Neural network approximations to posterior densities: an analytical approach
AbstractIn Hoogerheide, Kaashoek and Van Dijk (2002) the class of neural networksampling methods is introduced to sample from a target (posterior)distribution that may be multi-modal or skew, or exhibit strong correlationamong the parameters. In these methods the neural network is used as animportance function in IS or as a candidate density in MH. In this note wesuggest an analytical approach to estimate the moments of a certain (target)distribution, where `analytical' refers to the fact that no samplingalgorithm like MH or IS is needed.We show an example in which our analyticalapproach is feasible, even in a case where a `standard' Gibbs approach wouldfail or be extremely slow.
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Bibliographic InfoPaper provided by Erasmus University Rotterdam, Econometric Institute in its series Econometric Institute Report with number EI 2003-38.
Date of creation: 07 Aug 2003
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Markov chain Monte Carlo; Bayesian inference; importance sampling; neural networks;
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- van Dijk, H. K. & Kloek, T., 1980. "Further experience in Bayesian analysis using Monte Carlo integration," Journal of Econometrics, Elsevier, vol. 14(3), pages 307-328, December.
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