Bayesian Radial Basis Functions of
نویسندگان
چکیده
A Bayesian framework for the analysis of radial basis functions (RBF) is proposed which readily accommodates uncertainty in the dimension of the model. A distribution is deened over the space of all RBF models of a given basis function and posteriors are computed using reversible jump Markov chain Monte Carlo samplers (Green, 1995). This alleviates the need to select one particular architecture during the modeling process. We show that the resulting models are relatively free from user set design parameters and that they exhibit good performance characteristics on a number of benchmark test series .
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