Introduction To Bayesian Statistics

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"…this edition is useful and effective in teaching Bayesian inference at both elementary and intermediate levels

Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used

In addition, more advanced topics in the field are presented in four new chapters Bayesian inference for a normal with unknown mean and variance Bayesian inference for a Multivariate Normal mean vector Bayesian inference for the Multiple Linear Regression Model and Computational Bayesian Statistics including Markov Chain Monte Carlo

In this third Edition, four newly-added chapters address topics that reflect the rapid advances in the field of Bayesian statistics

Introduction to Bayesian Statistics, Third Edition also features Topics including the Joint Likelihood function and inference using independent Jeffreys priors and join conjugate prior The cutting-edge topic of computational Bayesian Statistics in a new chapter, with a unique focus on Markov Chain Monte Carlo methods Exercises throughout the book that have been updated to reflect new applications and the latest software applications Detailed appendices that guide readers through the use of R and Minitab software for Bayesian analysis and Monte Carlo simulations, with all related macros available on the book's website Introduction to Bayesian Statistics, Third Edition is a

It can also be used as a reference work for statisticians who require a working knowledge of Bayesian statistics.

It is a well-written book on elementary Bayesian inference, and the material is easily accessible

It is both concise and timely, and provides a good collection of overviews and reviews of important tools used in Bayesian statistical methods. There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most introductory statistics texts only present frequentist methods

Minitab macros and R functions are available on the book's related website to assist with chapter exercises

The authors continue to provide a Bayesian treatment of introductory statistical topics, such as scientific data gathering, discrete random variables, robust Bayesian methods, and Bayesian approaches to inference for discrete random variables, binomial proportions, Poisson, and normal means, and simple linear regression

The inclusion of these topics will facilitate readers' ability to advance from a minimal understanding of Statistics to the ability to tackle topics in more applied, advanced level books