The branch of statistical inference which employs the principles of Bayes' theorem for updating the probability for a hypothesis is known as Bayesian inference. Various techniques and methods studied under this discipline include Markov chains, Monte Carlo methods, Bayesian estimation, hierarchical models, decision theory and hypothesis testing. Bayesian inference finds extensive applications across various fields such as artificial intelligence, e-mail security, gene analysis and jurimetrics. Its is also used in the areas related to phylogeny, methylation analysis, chemical kinetics and stock market predictions. There has been rapid progress in this field and its applications are finding their way across multiple industries. From theories to research to practical applications, case studies related to all contemporary topics of relevance to this field have been included in this book. It aims to equip students and experts with the advanced topics and upcoming concepts in this area.