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.

About the Author

Herbert K. H. Lee is an Assistant Professor of Applied Mathematics and Statistics at the University of California, Santa Cruz. His major research interest is in the field of Bayesian statistics, with primary emphases on spatial inverse problems and connections between statistics and machine learning. While at the Institute of Statistics and Decision Science at Duke, he was part of the NSF/KDI-funded project Multi-Scale Modeling and Simulation in Scientific Inference: Hierarchical Methods for Parameter Estimation in Porous Flow, and he has published journal articles on topics in both Bayesian statistics and machine learning.

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