Bayesian

There has been a dramatic growth in the development and application of Bayesian inferential methods. Some of this growth is due to the availability of powerful simulation-based algorithms to summarize posterior distributions. There has been also a growing interest in the use of the system R for statistical analyses. R's open source nature, free availability, and large number of contributor packages have made R the software of choice for many statisticians in education and industry.

This book introduces recent developments in Bayesian inference, and covers a variety of topics including robust Bayesian estimation, solving inverse problems via Bayesian theories, hierarchical Bayesian inference, and its applications for scattering experiments. The book will stimulate more extensive research on Bayesian fronts to include theories, methods, computational algorithms and applications in various fields such as data science, AI, machine learning, and causality analysis.

This book introduces key ideas of Bayesian sampling methods, Bayesian estimation, and selection of the prior. This volume is designed for statisticians, engineers, doctors, and machine learning researchers.

Learn the concepts and practical side of using the Bayesian approach to estimate likely event outcomes.

Contributors to this volume elucidate various new developments in these aspects of Bayesian networks (BNs) .