Bayesian

The use of Bayesian methods in applied statistical analysis has become increasingly popular, yet most introductory statistics texts continue to only present the subject using frequentist methods. Introduction to Bayesian Statistics, Second Edition focuses on Bayesian methods that can be used for inference, and it also addresses how these methods compare favorably with frequentist alternatives. Teaching statistics from the Bayesian perspective allows for direct probability statements about parameters, and this approach is now more relevant than ever due to computer programs that allow practitioners to work on problems that contain many parameters.

This volume is a collection of articles on system and Bayesian reliability analysis. The book is dedicated to Emeritus Professor Richard E. Barlow, who is well known for his pioneering research on reliability theory and Bayesian reliability analysis.

by Sylvia Frühwirth-Schnatter (Editor), Angela Bitto (Editor), Gregor Kastner (Editor), Alexandra Posekany (Editor)

Apply advanced techniques for optimizing machine learning processes. Bayesian optimization helps pinpoint the best configuration for your machine learning models with speed and accuracy.