Bayesian Nonparametrics via Neural Networks

Bayesian Nonparametrics via Neural Networks is the first book to focus on neural networks in the context of nonparametric regression and classification, working within the Bayesian paradigm. It discusses neural networks in a statistical context, an approach in contrast to existing books, which tend to treat neural networks as a machine learning algorithm instead of a statistical model. Once this underlying statistical model is recognized, other standard statistical techniques can be applied to improve the model. The Bayesian approach allows better accounting for uncertainty. This book covers uncertainty in model choice and ways to deal with this issue, exploring ideas from statistics and machine learning. An analysis on the choice of prior and new noninformative priors is included, along with a substantial literature review. Written for statisticians using statistical terminology, this book will lead statisticians to an increased understanding of the neural network model and its applicability to real-world problems.

• Suitable for practising statisticians and researchers, computational scientists, and data miners, as well as graduate students preparing for these roles
• No prior knowledge of neural networks is assumed and topics are introduced in a self-contained manner, with references provided for further details
• Two examples are used to illustrate the major mathematical concepts throughout the book, one on ozone pollution and the other on credit applications