An incomplete taxonomy of Bayesian models with examples from industrial statistics applications

KEY POINT

We consider a taxonomy of data types, structural components (including hierarchical levels), and stochastic components for developing Bayesian models for data analysis.

About the author

M. S. Hamada is a Scientist and holds a PhD in Statistics from the University of Wisconsin-Madison. He is a Fellow of the American Statistical Association, of the American Society for Quality, and of Los Alamos National Laboratory. His research interests include design and analysis of experiments, measurement system assessment, quality control, and reliability.

Acknowledgments

We thank C.C. Essix for her support and encouragement of this work. We also thank Peter Marcy for a helpful discussion and reference and Claire Bowen, Isaac Michaud, Ken Ryan, Rich Warr, and Brian Weaver for helpful comments.