Bayesian Inference Methods - Applications in Data Science: Reviewing Bayesian inference methods and their applications in various data science tasks such as classification and regression

Abstract

Bayesian inference methods play a crucial role in modern data science, offering a principled framework for probabilistic reasoning and model estimation. This paper provides a comprehensive review of Bayesian inference methods and their applications in various data science tasks, with a focus on classification and regression. We discuss the theoretical foundations of Bayesian inference, including Bayes' theorem, prior and posterior distributions, and Markov chain Monte Carlo (MCMC) techniques. We then explore how these methods are applied in practice, highlighting their advantages and limitations. Finally, we discuss future research directions and the potential impact of Bayesian inference on the field of data science.