Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
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We investigate the setting in which Monte Carlo methods are used and draw a parallel to the formal setting of statistical inference. In particular, we find that Monte Carlo approximation gives rise to a bias-variance dilemma.W e show that it is possible to construct a biased approximation scheme with a lower approximation error than a related unbiased algorithm.