By Herbert K. H. Lee

Bayesian Nonparametrics through Neural Networks is the 1st ebook to target neural networks within the context of nonparametric regression and type, operating in the Bayesian paradigm. Its target is to demystify neural networks, placing them firmly in a statistical context instead of treating them as a black field. This technique is unlike current books, which are likely to deal with neural networks as a computer studying set of rules rather than a statistical version. as soon as this underlying statistical version is well-known, different usual statistical concepts will be utilized to enhance the version.

The Bayesian process permits larger accounting for uncertainty. This booklet covers uncertainty in version selection and techniques to accommodate this factor, exploring a few rules from records and computing device studying. an in depth dialogue at the number of previous and new noninformative priors is integrated, besides a considerable literature evaluate. Written for statisticians utilizing statistical terminology, Bayesian Nonparametrics through Neural Networks will lead statisticians to an elevated realizing of the neural community version and its applicability to real-world difficulties.

To illustrate the main mathematical strategies, the writer makes use of examples in the course of the booklet: one on ozone toxins and the opposite on credits purposes. The method tested is correct for regression and classification-type difficulties and is of curiosity end result of the frequent strength purposes of the methodologies defined within the e-book.

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**Additional resources for Bayesian nonparametrics via neural networks**

**Example text**

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