Handbook of Markov Chain Monte Carlo
暫譯: 馬可夫鏈蒙地卡羅手冊

這本《馬可夫鏈蒙地卡羅手冊》的第二版經過徹底修訂和擴充，反映了自第一版出版以來MCMC方法的劇變。隨著兩位新編輯Radu V. Craiu和Dootika Vats的加入，這本綜合性參考書現在提供了對理論基礎和前沿發展的更深入見解，這些發展正在重塑該領域。

**主要特點：**
- 完全重組的內容，包括13個來自第一版的更新章節和10個全新的章節，反映最新的方法進展
- 深入探討多模態取樣、難以處理的似然問題和反演MCMC理論的最新突破
- 全面探索無偏MCMC方法、控制變數和嚴格的收斂界限
- 提供在現代硬體和軟體平台上實施MCMC算法的實用指導
- 包含MCMC與深度學習及其他機器學習方法整合的前沿材料
- 對理論基礎和實際實施策略的權威處理

這本必備的參考書服務於統計學家、計算機科學家、物理學家、數據科學家以及跨學科的研究人員，他們使用計算方法進行貝葉斯推斷和隨機模擬。研究生會發現這是寶貴的學習資源，而經驗豐富的從業者則會欣賞其理論深度與實際實施建議之間的平衡。無論是作為當前MCMC方法論的綜合指南，還是作為特定高級技術的參考，這本手冊都為任何在貝葉斯計算和現代統計建模交集工作的專業人士提供了權威資源。

Radu V. Craiu is a professor of statistics at the University of Toronto. His research interests are in computational methods in statistics, statistical inference, copula models, model selection procedures, and the use of statistical methods for scientific advancement in genetics, astronomy and demography. He is currently Contributing Editor for the IMS Bulletin and Associate Editor for the Harvard Data Science Review, Journal of Computational and Graphical Statistics, Statistics Surveys, The Canadian Journal of Statistics, and Statistical Methods and Applications. He received the CRM-SSC prize, is a Fellow of the Institute of Mathematical Statistics, a Fellow of the American Statistical Association, a Faculty Affiliate of the Vector Institute, and an Elected Member of the International Statistical Institute.

Dootika Vats is an associate professor in the Department of Mathematics and Statistics at the Indian Institute of Technology Kanpur, India. Her research interests include output analysis for stochastic simulation, Markov chain Monte Carlo methods, proximal methods in Bayesian computation, and stochastic optimization. In 2021, she was one of the winners of the Blackwell-Rosenbluth Award given by the junior-International Society for Bayesian Analysis. She currently serves as an Associate Editor for Bayesian Analysis, Journal of Computational and Graphical Statistics, and Sankhya B.

Galin L. Jones is Lynn Y. S. Lin Professor of Statistics and Director of the School of Statistics at the University of Minnesota. His primary research interests include Markov chain Monte Carlo, statistical theory and methods in both Bayesian and frequentist domains, as well as applications in neuroimaging and the physical sciences. He has collaborated with a wide range of researchers, including psychologists, veterinarians, librarians, ecologists, and astrophysicists, among others. Jones is an elected fellow of both the American Statistical Association and the Institute for Mathematical Statistics and is past Co-Editor of the Journal of Computational and Graphical Statistics.

Steve Brooks is director and founder of Select Statistics, a statistical consultancy business based in the United Kingdom. He was formerly professor of Statistics at Cambridge University and received the Royal Statistical Society Guy medal in Bronze in 2005 and the Philip Leverhulme prize in 2004. Like his co-editors, he has served on numerous professional committees both in the United Kingdom and elsewhere, as well as sitting on numerous editorial boards. He is co-author of Bayesian Analysis for Population Ecology (Chapman & Hall/CRC, 2009) and co-founder of the National Centre for Statistical Ecology. His research interests include the development and application of computational statistical methodology across a broad range of application areas.

Andrew Gelman is a professor of statistics and political science at Columbia University. His books include Bayesian Data Analysis (with John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin), Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do (with David Park, Boris Shor, and Jeronimo Cortina), Regression and Other Stories (with Jennifer Hill and Aki Vehtari), Active Statistics (with Aki Vehatri), and the forthcoming BayesianWorkflow (with many collaborators). He has done research on applications ranging from elections and public opinion to laboratory assays and toxicology; on the theory and practice of Bayesian statistical methods, from design and data collection through modeling, analysis, and model evaluation; and on statistical computing, graphics, and communication.

Xiao-Li Meng is the Whipple V. N. Jones Professor of Statistics at Harvard, and the Founding Editor-in-Chief of Harvard Data Science Review. Meng received his BS in mathematics from Fudan University in 1982 and his PhD in statistics from Harvard in 1990. He was on the faculty of the University of Chicago from 1991 to 2001 before returning to Harvard, where he served as the Chair of the Department of Statistics (2004-2012) and the Dean of Graduate School of Arts and Sciences (2012-2017). His interests range from the theoretical foundations of statistical inferences (e.g., the interplay among Bayesian, Fiducial, and frequentist perspectives; frameworks for multi-source, multi-phase and multiresolution inferences) to statistical methods and computation (e.g., posterior predictive pvalue; EM algorithm; MCMC; bridge and path sampling) to applications in natural, social, and medical sciences and engineering (e.g., complex statistical modeling in astronomy and astrophysics, assessing disparity in mental health services, and quantifying statistical information in genetic studies). Meng was named the best statistician under the age of 40 by Committee of Presidents of Statistical Societies (COPSS) in 2001, and he was elected to the American Academy of Arts and Sciences in 2020.

**Radu V. Craiu** 是多倫多大學的統計學教授。他的研究興趣包括統計中的計算方法、統計推斷、聯合模型、模型選擇程序，以及統計方法在遺傳學、天文學和人口統計學中的科學進步應用。他目前擔任IMS Bulletin的貢獻編輯，以及哈佛數據科學評論、計算與圖形統計期刊、統計調查、加拿大統計期刊和統計方法與應用的副編輯。他獲得了CRM-SSC獎，並且是數學統計學會的會士、美國統計協會的會士、Vector Institute的教職夥伴，以及國際統計學會的當選成員。

**Dootika Vats** 是印度坎普爾印度理工學院數學與統計系的副教授。她的研究興趣包括隨機模擬的輸出分析、馬可夫鏈蒙特卡羅方法、貝葉斯計算中的近端方法，以及隨機優化。2021年，她是由年輕的國際貝葉斯分析學會頒發的Blackwell-Rosenbluth獎的獲獎者之一。她目前擔任貝葉斯分析、計算與圖形統計期刊和Sankhya B的副編輯。

**Galin L. Jones** 是明尼蘇達大學的Lynn Y. S. Lin統計學教授及統計學院院長。他的主要研究興趣包括馬可夫鏈蒙特卡羅、貝葉斯和頻率主義領域的統計理論與方法，以及在神經影像學和物理科學中的應用。他與各類研究人員合作，包括心理學家、獸醫、圖書館員、生態學家和天體物理學家等。Jones是美國統計協會和數學統計學會的當選會士，並曾擔任計算與圖形統計期刊的共同編輯。

**Steve Brooks** 是位於英國的Select Statistics統計顧問公司的創始人及主管。他曾是劍橋大學的統計學教授，並於2005年獲得英國皇家統計學會的銅獎Guy Medal，以及於2004年獲得Philip Leverhulme獎。與他的共同編輯一樣，他曾在英國及其他地區的多個專業委員會任職，並擔任多個編輯委員會的成員。他是《人口生態的貝葉斯分析》（Chapman & Hall/CRC, 2009）的共同作者，也是國家統計生態中心的共同創辦人。他的研究興趣包括在廣泛應用領域中開發和應用計算統計方法。

**Andrew Gelman** 是哥倫比亞大學的統計學和政治學教授。他的著作包括《貝葉斯數據分析》（與John Carlin、Hal Stern、David Dunson、Aki Vehtari和Donald Rubin合著）、《紅州、藍州、富州、窮州：為什麼美國人以這種方式投票》（與David Park、Boris Shor和Jeronimo Cortina合著）、《回歸與其他故事》（與Jennifer Hill和Aki Vehtari合著）、《主動統計》（與Aki Vehtari合著）以及即將出版的《貝葉斯工作流程》（與多位合作者合著）。他的研究涵蓋從選舉和公共意見到實驗室檢測和毒理學的應用；貝葉斯統計方法的理論與實踐，從設計和數據收集到建模、分析和模型評估；以及統計計算、圖形和溝通。

**Xiao-Li Meng** 是哈佛大學的Whipple V. N. Jones統計學教授，也是哈佛數據科學評論的創始主編。Meng於1982年獲得復旦大學數學學士學位，並於1990年獲得哈佛大學統計學博士學位。他於1991年至2001年在芝加哥大學任教，之後回到哈佛，擔任統計系主任（2004-2012）和文理研究生院院長（2012-2017）。他的研究興趣範圍從統計推斷的理論基礎（例如，貝葉斯、信賴和頻率主義觀點之間的相互作用；多來源、多階段和多解析度推斷的框架）到統計方法和計算（例如，後驗預測p值；EM算法；MCMC；橋接和路徑取樣），再到自然科學、社會科學和醫學工程的應用（例如，天文學和天體物理學中的複雜統計建模、評估心理健康服務的差異，以及量化遺傳研究中的統計信息）。Meng於2001年被統計學會會長委員會（COPSS）評選為40歲以下最佳統計學家，並於2020年當選美國藝術與科學學院成員。