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With growing interest in data mining and its merits, including the incorporation of historical or experiential information into statistical analysis, Bayesian inference has become an important tool for analyzing complicated data and solving inverse problems in various fields such as artificial intelligence. 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. We hope that this 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.
Development of Bayesian Inference
Bayesian Inference for Inverse Problems
Robust Bayesian Estimation
Applications of Hierarchical Bayesian Methods to Answer Multilayer Questions with Limited Data
Bayesian Inference as a Tool to Optimize Spectral Acquisition in Scattering Experiments