By Herbert K. H. Lee
Bayesian Nonparametrics through Neural Networks is the 1st e-book to target neural networks within the context of nonparametric regression and type, operating in the Bayesian paradigm. It discusses neural networks in a statistical context, an technique unlike current books, which are likely to deal with neural networks as a laptop studying set of rules rather than a statistical version. as soon as this underlying statistical version is famous, different typical statistical innovations could be utilized to enhance the version. The Bayesian technique permits higher accounting for uncertainty. This ebook covers uncertainty in version selection and how one can take care of this factor, exploring rules from data and desktop studying. An research at the selection of previous and new noninformative priors is integrated, besides a considerable literature assessment. Written for statisticians utilizing statistical terminology, this booklet will lead statisticians to an elevated figuring out of the neural community version and its applicability to real-world difficulties.
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