Introduction to Bayesian Statistics 3/e (Hardcover)

"...this edition is useful and effective in teaching Bayesian inference at both elementary and intermediate levels. 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. Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used. In this third Edition, four newly-added chapters address topics that reflect the rapid advances in the field of Bayesian statistics. 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. 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. 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. Minitab macros and R functions are available on the book's related website to assist with chapter exercises. 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 textbook for upper-undergraduate or first-year graduate level courses on introductory statistics course with a Bayesian emphasis. It can also be used as a reference work for statisticians who require a working knowledge of Bayesian statistics.

「...這本書在教授貝葉斯推論的初級和中級課程上非常有用和有效。它是一本寫得很好的初級貝葉斯推論書籍，內容易於理解。它既簡潔又及時，提供了一個很好的概述和評論貝葉斯統計方法中重要工具的集合。」

「在應用統計分析中，貝葉斯方法的使用有了強烈的上升趨勢，然而大多數入門統計學教材只介紹頻率主義方法。貝葉斯統計學有許多重要的優勢，如果學生將進入需要使用統計學的領域，他們應該學習這些優勢。在這第三版中，新增了四個章節，涵蓋了貝葉斯統計領域的快速發展。作者們繼續以貝葉斯方法處理入門統計學的主題，例如科學數據收集、離散隨機變量、魯棒貝葉斯方法以及對於離散隨機變量、二項比例、泊松分佈和正態均值以及簡單線性回歸的貝葉斯推論方法。此外，該領域的更高級主題在四個新章節中呈現：對於未知均值和變異數的正態分佈的貝葉斯推論；多變量正態分佈均值的貝葉斯推論；多元線性回歸模型的貝葉斯推論；以及包括馬爾可夫鏈蒙特卡羅在內的計算貝葉斯統計。這些主題的包含將有助於讀者從對統計學的最基本理解進一步提升，能夠應對更應用和高級水平的書籍中的主題。書中的Minitab宏和R函數可在相關網站上找到，以協助章節練習。《貝葉斯統計學導論，第三版》還包括以下特點：」

「包括聯合似然函數和使用獨立Jeffreys先驗和聯合共軛先驗進行推論的尖端主題。」

「全新章節中的計算貝葉斯統計學，獨特關注馬爾可夫鏈蒙特卡羅方法。」

「全書中的練習已更新以反映新的應用和最新的軟體應用。」

「詳細的附錄指導讀者如何使用R和Minitab軟體進行貝葉斯分析和蒙特卡羅模擬，所有相關的宏都可在書籍的網站上找到。」

「《貝葉斯統計學導論，第三版》是一本以貝葉斯為重點的高年級本科或研究生入門統計學課程教材。它也可以作為需要掌握貝葉斯統計學的統計學家的參考書。」