Shows both the shortcomings and benefits of each technique, and even demonstrates useful combinations of the two.

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...

This text presents a concise introduction to the concepts of probability theory and mathematical statistics.

How does a marble manufacturer know that the color will be consistent throughout the products being made? How can you tell if liquid at the bottom of a container is the same consistency as at the top? How does a pellet manufacturer know if the pellets are consistently the same size? How does a chemical manufacturer know if the percent purity in a sample is representative of the whole batch? These and similar questions are answered in A Primer for Sampling Solids, Liquids, and Gases: Based on the Seven Sampling Errors of Pierre Gy. Author Patricia L. Smith illustrates what to look for in...

Takes the reader step by step through the techniques of eliciting and analyzing expert judgment,

Engineering reliability concerns failure data analysis, the economics of maintenance policies, and system reliability. This textbook develops the use of probability and statistics in engineering reliability and maintenance problems. The author uses probability models in the analysis of failure data, decisions relative to planned maintenance, and prediction relative to preliminary design. Some of the outstanding features include the analysis of failure data for both continuous and discrete probability from a finite population perspective, probability models derived from engineering...

A selection of studies by professionals in the semiconductor industry illustrating the use of statistical methods to improve manufacturing processes.

Statisticians know that the clean data sets that appear in textbook problems have little to do with real-life industry data. To better prepare their students for all types of statistical careers, academic statisticians now strive to use data sets from real-life statistical problems. This book contains 20 case studies that use actual data sets that have not been simplified for classroom use. Each case study is a collaboration between statisticians from academe and from business, industry, or government. This book is the result of a collaborative workshop of statisticians focusing on...

Adaptive statistical tests, developed over the last 30 years, are often more powerful than traditional tests of significance, but have not been widely used. To date, discussions of adaptive statistical methods have been scattered across the literature and generally do not include the computer programs necessary to make these adaptive methods a practical alternative to traditional statistical methods. Until recently, there has also not been a general approach to tests of significance and confidence intervals that could easily be applied in practice. Modern adaptive methods are more general...

This book presents and demonstrates the use of functions within a MATLAB computational environment.