Foundations of Bayesian Statistics for Data Scientists: With R and Python (Chapman & Hall/CRC Texts in Statistical Science)

This book is an overview of the Bayesian approach to applying the most important inferential methods of statistical science.It is designed as a textbook for advanced undergraduate and master’s students in Data Science, Statistics, or Mathematics who are interested in learning about Bayesian statistics. The reader should be familiar with calculus and should have taken a statistical inference Statistics course covering the basic rules of probability, probability distributions and expectations, as well as the fundamentals of the traditional, frequentist approach to statistics, including sampling distributions, likelihood functions, basic inferential methods such as point estimation, confidence intervals, significance tests, and linear regression models. Key Features:? Uses real world data examples and contains numerous exercises. ? Includes software appendices in R and Python. ? Offers slides, labs, and other materials on the book’s website (https://bayes4ds.rwth-aachen.de). Each chapter