Bayesian Statistical Methods

Bayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. This book focuses on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized linear models (GLM). The authors include many examples with complete R code and comparisons with analogous frequentist procedures.

In addition to the basic concepts of Bayesian inferential methods, the book covers many general topics:

• Advice on selecting prior distributions
• Computational methods including Markov chain Monte Carlo (MCMC)
• Model-comparison and goodness-of-fit measures, including sensitivity to priors
• Frequentist properties of Bayesian methods

Case studies covering advanced topics illustrate the flexibility of the Bayesian approach:

• Semiparametric regression
• Handling of missing data using predictive distributions
• Priors for high-dimensional regression models
• Computational techniques for large datasets
• Spatial data analysis

The advanced topics are presented with sufficient conceptual depth that the reader will be able to carry out such analysis and argue the relative merits of Bayesian and classical methods. A repository of R code, motivating data sets, and complete data analyses are available on the book's website.

Brian J. Reich, Associate Professor of Statistics at North Carolina State University, is currently the editor-in-chief of the Journal of Agricultural, Biological, and Environmental Statistics and was awarded the LeRoy & Elva Martin Teaching Award.

Sujit K. Ghosh, Professor of Statistics at North Carolina State University, has over 22 years of research and teaching experience in conducting Bayesian analyses, received the Cavell Brownie mentoring award, and served as the Deputy Director at the Statistical and Applied Mathematical Sciences Institute.

《貝葉斯統計方法》提供了數據科學家進行貝葉斯分析所需的基礎和計算工具。本書重點介紹了在實踐中常用的貝葉斯方法，包括多元線性回歸、混合效應模型和廣義線性模型（GLM）。作者提供了許多帶有完整 R 代碼的示例，並與類似的頻率主義程序進行了比較。

除了貝葉斯推論方法的基本概念外，本書還涵蓋了許多一般性主題：
- 選擇先驗分布的建議
- 包括馬爾可夫鏈蒙特卡羅（MCMC）在內的計算方法
- 模型比較和適配度測量，包括對先驗分布的敏感性
- 貝葉斯方法的頻率主義性質

通過案例研究，本書展示了貝葉斯方法的靈活性，涵蓋了一些高級主題：
- 半參數回歸
- 使用預測分布處理缺失數據
- 高維回歸模型的先驗分布
- 大數據集的計算技術
- 空間數據分析

這些高級主題以足夠的概念深度呈現，讀者將能夠進行此類分析並論證貝葉斯方法和傳統方法的相對優勢。書籍網站提供了 R 代碼庫、激勵數據集和完整的數據分析。

布萊恩·J·賴希（Brian J. Reich）是北卡羅來納州立大學統計學副教授，目前擔任《農業、生物和環境統計學雜誌》的主編，並獲得了勒羅伊和埃爾瓦·馬丁教學獎。

蘇吉特·K·戈許（Sujit K. Ghosh）是北卡羅來納州立大學統計學教授，擁有超過22年的貝葉斯分析研究和教學經驗，獲得了卡維爾·布朗尼（Cavell Brownie）指導獎，並曾擔任統計和應用數學科學研究所的副主任。

Brian J. Reich, Associate Professor of Statistics at North Carolina State University, is currently the editor-in-chief of the Journal of Agricultural, Biological, and Environmental Statistics and was awarded the LeRoy & Elva Martin Teaching Award.

Sujit K. Ghosh, Professor of Statistics at North Carolina State University, has over 22 years of research and teaching experience in conducting Bayesian analyses, received the Cavell Brownie mentoring award, and served as the Deputy Director at the Statistical and Applied Mathematical Sciences Institute

Brian J. Reich，北卡羅來納州立大學統計學副教授，目前擔任《農業、生物和環境統計學期刊》的主編，並獲得了LeRoy＆Elva Martin教學獎。

Sujit K. Ghosh，北卡羅來納州立大學統計學教授，擁有超過22年的研究和教學經驗，進行貝葉斯分析，獲得Cavell Brownie指導獎，並擔任統計和應用數學科學研究所的副主任。