Thomas Melluish

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When correct priors are known, Bayesian algorithms give optimal decisions, and accurate confidence values for predictions can be obtained. If the prior is incorrect however, these confidence values have no theoretical base – even though the algorithms' predictive performance may be good. There also exist many successful learning algorithms which only depend(More)
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