Preface

Bayesian inference provides a unified framework to deal with all sorts of uncertainties when learning patterns from data using machine learning models and using it for predicting future observations. However, learning and implementing Bayesian models is not easy for data science practitioners due to the level of mathematical treatment involved. Also, applying Bayesian methods to real-world problems requires high computational resources. With the recent advancements in cloud and high-performance computing and easy access to computational resources, Bayesian modeling has become more feasible to use for practical applications today. Therefore, it would be advantageous for all data scientists and data engineers to understand Bayesian methods and apply them in their projects to achieve better results.