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11

IJCNN
2000
IEEE

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On MCMC Sampling in Bayesian MLP Neural Networks

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On MCMC Sampling in Bayesian MLP Neural Networks

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Bayesian MLP neural networks are a ﬂexible tool in complex nonlinear problems. The approach is complicated by need to evaluate integrals over high-dimensional probability distributions. The integrals are generally approximated with Markov Chain Monte Carlo (MCMC) methods. There are several practical issues which arise when implementing MCMC. This article discusses the choice of starting values and the number of chains in Bayesian MLP models. We propose a new method for choosing the starting values based on early stopping and we demonstrate the beneﬁts of using several independent chains.

Aki Vehtari, Simo Särkkä, Jouko Lampinen

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Added	31 Jul 2010
Updated	31 Jul 2010
Type	Conference
Year	2000
Where	IJCNN
Authors	Aki Vehtari, Simo Särkkä, Jouko Lampinen

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