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Abstract:
In this paper, we propose a full Bayesian model for neural
networks. This model treats the model dimension (number of
neurons), model parameters, regularisation parameters and noise
parameters as random variables that need to be estimated. We then
propose a reversible jump Markov chain Monte Carlo (MCMC) method to
perform the necessary computations. We find that the results are
not only better than the previously reported ones, but also appear
to be robust with respect to the prior specification. Moreover, we
present a geometric convergence theorem for the algorithm.
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