Bayesian Statistical Methods: With Applications to Machine Learning
暫譯: 貝葉斯統計方法：應用於機器學習

這本書為資料科學家提供了進行貝葉斯分析所需的基礎和計算工具。與其他書籍相比，它更專注於在實務中常規應用的貝葉斯方法，包括多元線性回歸、混合效應模型和廣義線性模型。

Brian J. Reich, Gertrude M. Cox Distinguished Professor of Statistics at North Carolina State University, applies Bayesian statistical methods in a variety of fields including environmental epidemiology, engineering, weather and climate. He is a Fellow of the American Statistical Association, former Editor-in-Chief of the Journal of Agricultural, Biological, and Environmental Statistics and recipient of the LeRoy & Elva Martin Teaching Award at NC State University.

Sujit K. Ghosh, Professor of Statistics at North Carolina State University, has worked in advanced research fields such as Bayesian inference, spatial statistics, survival analysis and shape-constrained inference, addressing complex inferential challenges in biomedical and environmental sciences, econometrics, and engineering. At NC State, he has been honored with the D.D. Mason Faculty Award and the Cavell Brownie Mentoring Award, reflecting his excellence in research, mentoring and teaching. His leadership includes impactful service as Program Director at NSF's Division of Mathematical Sciences, Deputy Director at SAMSI and President of the IISA.

布萊恩·J·萊希（Brian J. Reich），北卡羅來納州立大學的格特魯德·M·考克斯統計學傑出教授，應用貝葉斯統計方法於多個領域，包括環境流行病學、工程、天氣和氣候。他是美國統計學會的會士，曾擔任《農業、生物和環境統計學期刊》的主編，並獲得北卡羅來納州立大學的勒羅伊與艾爾瓦·馬丁教學獎。

蘇吉特·K·戈什（Sujit K. Ghosh），北卡羅來納州立大學的統計學教授，曾在貝葉斯推斷、空間統計、生存分析和形狀約束推斷等先進研究領域工作，解決生物醫學和環境科學、計量經濟學及工程中的複雜推斷挑戰。在北卡羅來納州立大學，他獲得了D.D.梅森教職員獎和卡維爾·布朗尼導師獎，以表彰他在研究、指導和教學方面的卓越表現。他的領導經驗包括在美國國家科學基金會數學科學部擔任計畫主任、在SAMSI擔任副主任以及擔任國際統計學會（IISA）會長。