This book provides in-depth knowledge to solve engineering, geometrical, mathematical, and scientific problems with the help of advanced computational methods with focus on mechanical and materials engineering.
This book brings the power of modern Bayesian thinking, modeling, and computing to a broad audience. In particular, it is an ideal resource for advanced undergraduate statistics students and practitioners with comparable experience.
The Handbook of Bayesian Variable Selection provides a comprehensive review of theoretical, methodological and computational aspects of Bayesian methods for variable selection. It also provides a valuable reference for all interested in applying existing methods and/or pursuing methodological extensions.
