Reversible Jump MCMC Simulated Annealing for Neural Networks

@inproceedings{Andrieu2000ReversibleJM,
  title={Reversible Jump MCMC Simulated Annealing for Neural Networks},
  author={Christophe Andrieu and Nando de Freitas and Arnaud Doucet},
  booktitle={UAI},
  year={2000}
}
We propose a novel reversible jump Markov chain Monte Carlo (MCMC) simulated an­ nealing algorithm to optimize radial basis function (RBF) networks. This algorithm enables us to maximize the joint posterior distribution of the network parameters and the number of basis functions. It performs a global search in the joint space of the pa­ rameters and number of parameters, thereby surmounting the problem of local minima. We also show that by calibrating a Bayesian model, we can obtain the… CONTINUE READING
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