Titlebook: Bayesian Learning for Neural Networks; Radford M. Neal Book 1996 Springer Science+Business Media New York 1996 Fitting.Likelihood.algorith

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Priors for Infinite Networks,r hidden-to-output weights results in a Gaussian process prior for functions,which may be smooth, Brownian, or fractional Brownian. Quite different effects can be obtained using priors based on non-Gaussian stable distributions. In networks with more than one hidden layer, a combination of Gaussian and non-Gaussian priors appears most interesting.

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Monte Carlo Implementation,t hybrid Monte Carlo performs better than simple Metropolis,due to its avoidance of random walk behaviour. I also discuss variants of hybrid Monte Carlo in which dynamical computations are done using “partial gradients”, in which acceptance is based on a “window” of states,and in which momentum updates incorporate “persistence”.

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Lecture Notes in Statisticshttp://image.papertrans.cn/b/image/181856.jpg

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