Bayesian

Bayes Theorem, Bayesian networks, Bayesian sampling methods, Bayesian inference, machine learning and much more

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.

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

People apply Bayesian methods in many areas: from game development to drug discovery. They give superpowers to many machine learning algorithms: handling missing data, extracting much more information from small datasets. Bayesian methods also allow us to estimate uncertainty in predictions, which is a desirable feature for fields like medicine.

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