A Turing Award-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize artificial intelligence
"Correlation is not causation." This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality--the study of cause and effect--on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why.

http://www.hup.harvard.edu/catalog.php?isbn=9780674088917

《高等院校统计学专业规划教材•贝叶斯统计》共六章，可分二部分。前三章围绕先验分布介绍贝叶斯推断方法。后三章围绕损失函数介绍贝叶斯决策方法。阅读这些内容仅需要概率统计基本知识就够了。《高等院校统计学专业规划教材•贝叶斯统计》力图用生动有趣的例子来说明贝叶斯统计的基本思想和基本方法，尽量使读者对贝叶斯统计产生兴趣，引发读者使用贝叶方法去认识和解决实际问题的愿望。进而去丰富和发展贝叶斯统计。假如学生的兴趣被钓出来，愿望被引出来，那么讲授这一门课的目的也基本达到了。

《面向生态学数据的贝叶斯统计:层次模型、算法和R编程》内容简介：作为统计学的两大分支，频率论和贝叶斯统计创立的时间相差无几，但贝叶斯统计直到近10年才被逐步引进到生态学数据分析。《面向生态学数据的贝叶斯统计：层次模型、算法和R编程》涵盖方法引论与实验分析应用两部分，针对多个时空尺度，介绍了适合于生态学数据的统计推断方法和层次模型，涉及经典频率论和贝叶斯统计的模型、算法和具体编程。首先阐述了生态学数据的层次结构和时空变异性，以及频率论和贝叶斯统计。然后介绍贝叶斯推断的基础概念、分析框架和算法原理；并进一步针对生态学层次模型、时间序列及时空复合格局数据依次展开分析模拟。在应用操作部分，配合方法部分的各章内容介绍基于R的算法与编程实践。最后《面向生态学数据的贝叶斯统计：层次模型、算法和R编程》还附录了与生态学数据密切相关的频率论与贝叶斯统计的基础知识。
《面向生态学数据的贝叶斯统计：层次模型、算法和R编程》适用于生态学和环境科学专业的研究生和科研人员，可作为实验和观测数据分析的教材或参考书。具有一定概率论和贝叶斯统计基础及统计软件R应用编程技术的人员，对于理解和应用《面向生态学数据的贝叶斯统计：层次模型、算法和R编程》所涉及的相关方法是必要的。

中国统计出版社

Statistics lectures have been a source of much bewilderment and frustration for generations of students. This book attempts to remedy the situation by expounding a logical and unified approach to the whole subject of data analysis.
This text is intended as a tutorial guide for senior undergraduates and research students in science and engineering. After explaining the basic principles of Bayesian probability theory, their use is illustrated with a variety of examples ranging from elementary parameter estimation to image processing. Other topics covered include reliability analysis, multivariate optimization, least-squares and maximum likelihood, error-propagation, hypothesis testing, maximum entropy and experimental design.
The Second Edition of this successful tutorial book contains a new chapter on extensions to the ubiquitous least-squares procedure, allowing for the straightforward handling of outliers and unknown correlated noise, and a cutting-edge contribution from John Skilling on a novel numerical technique for Bayesian computation called 'nested sampling'.

对统计学的学习来说，最主要的是掌握统计思想，理解相关的统计原理，能够根据实际情境提出解决问题的一个或几个合适方案，并懂得选择其中的最优。因此适合非统计专业学生的统计学理想教材，应该是能兼顾专业特点、深入浅出阐述统计学基本原理和方法，同时在轻快风趣的讲述中激发读者的学习兴趣，培养统计思维，并辅之例题分析，对使用中容易发生的错误加以提醒，切实提高学生应用统计方法分析解决实际问题的能力。《行为统计学基础》（第9版）正是这样一本非常出色的教材。本书写作风格轻松活泼，语言流畅易懂，数学深入浅出，读者在学习和阅读时不会感到枯燥乏味。
本书是心理和教育统计学方面的一本优秀的基础教材，对于在社会科学领域中的广大研究人员来说，也是一本不可多得的重要参考书

对国内的大多数从事生态研究的学者和研究生而言，数学方法的选择是他们在研究中所面临的最大的难题之一，因此在实验设计阶段因没有充分考虑不同方法对数据的要求，导致最后的试验结果无法做较为深入的分析，这也是国内生态学者的研究成果在国外期刊发表比较难以发表的主要原因之一。本书以目前生态学研究中最为重要的空间分析为主题，系统地介绍了目前生态学中常用的数学方法，因为作者是生态学家而非数学家，因此从生态学的角度对这些数学方法的介绍更容易被生态学家所理解和接受。本书是迄今为止并不多见的对生态学中常用的空间分析方法进行系统、全面、深入浅出介绍的专著，正如本书的名字所示，本书完全可以作为一本生态学家的指南。无疑本书的翻译出版将有利于推进空间分析方法在生态学中更为科学、有效地应用。

The primary purpose of this book is to prepare students for empirical research in economics. But it also equips those who plan to specialize in econometric theory and those whose empirical interests are in sociology or business. Aimed at first-year graduate students and advanced undergraduates, "A Course in Econometrics" covers the fundamentals - classical regression and simultaneous equations - but also contains clear treatments of symptotic theory and nonlinear regression. The innovative features of the text include: (1) a focus on the conditional expectation function and best linear predictor as interesting characteristics of a population: (2) a thoughtful interpretation of assumptions on the disturbance term in linear regression; (3) a consistent development of estimators as sample analogs of population parameters; (4) a careful discussion of alternative sampling schemes to clarify the distinction between random and idea explanatory variables; (5) a development of asymptotic theory that emphasizes a simple prototypical case; (6) a cautionary treatment of the use and abuse of significance tests; (7) an introduction to simultaneous equation models that focuses on structural parameters as invariant across populations. The text includes instructive exercises and graphs, along with several complete data sets.

《高等学校教材：SPSS统计分析高级教程（第2版）》以IBMSPSSStatistics20中文版为基础，全面、系统地介绍了各种多变量统计模型、多元统计分析模型、智能统计分析方法的原理和软件实现。在书中作者结合自身多年的统计分析实战和SPss行业应用经验，侧重于对统计新方法、新观点的讲解。在保证统计理论严谨的同时，又充分注重了文字的浅显易懂，使《高等学校教材：SPSS统计分析高级教程（第2版）》更加易学易用。
《高等学校教材：SPSS统计分析高级教程（第2版）》是一本如何使用SPss进行高级统计分析的指导书。读者可在www.StatStar.com下载书中案例数据，从而完整地重现全部分析内容，并可进一步在新浪微博与作者、其他读者进行讨论。
《高等学校教材：SPSS统计分析高级教程（第2版）》适合于已具备统计分析基础知识的读者阅读，可作为高等学校各专业高年级本科生、研究生的统计学教材或参考书，以及市场营销、金融、财务、人力资源管理等行业中需要做数据分析的人士，或从事咨询、研究、分析等专业人士的参考书。

Manfred D. Laubichler, Science
[A] brilliant biography, one can hardly imagine a better summary of Karl Pearson's fascinating life and complicated persona. --This text refers to the Hardcover edition.
Review
John Aldrich American Scientist : Exceeds all expectations in recreating the intellectual worlds in which Pearson tried to find a home.
Manfred D. Laubichler Science : [A] brilliant biography, one can hardly imagine a better summary of Karl Pearson's fascinating life and complicated persona.
Peter J. Bowler Nature : Highlights the complex route by which [Pearson's] quest for emotional and intellectual satisfaction led him towards . . . modern statistics.
Jenny Marie Journal of the History of Biology : This book is a remarkable achievement.
Richard J. Cleary The American Statistician : Very effectively conveys . . . that . . . [statistics allows students] to see the world in a new and beautiful way.
Ramachandran Bharath MAA Reviews : Theodore Porter's Karl Pearson explores the fullness and richness of Pearson's intellectual and emotional life.

Review
Journal of Modern History : The book is a pleasure to read: the prose sparkles; the protagonists are vividly drawn; the illustrations are handsome and illuminating; the insights plentiful and sharp. This will remain the definitive work on the early development of mathematical statistics for some time to come.
--Lorraine J. Daston
Science : An exceptionally searching, almost loving, study of the relevant inspirations and aberrations of its principal characters James Bernoulli, de Moivre, Bayes, Laplace, Gauss, Quetelet, Lexis, Galton, Edgeworth, and Pearson, not neglecting a grand supporting cast...The definitive record of an intellectual Golden Age, an overoptimistic climb to a height not to be maintained.
--M. Stone
New York Times Book Review : One is tempted to say that the history of statistics in the nineteenth century will be associated with the name Stigler.
--Morris Kline
Contemporary Psychology : In this tour de force of careful scholarship, Stephen Stigler has laid bare the people, ideas, and events underlying the development of statistics...He has written an important and wonderful book...Sometimes Stigler's prose is so evocative it is almost poetic.
--Howard Wainer
Review
Stigler's book exhibits a rare combination of mastery of technical materials, sensitivity to conceptual milieu, and near exhaustive familiarity with primary sources. An exemplary study
--Lorraine Daston

Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal connections and statistical associations. Cited in more than 2,100 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. In this revised edition, Judea Pearl elucidates thorny issues, answers readers' questions, and offers a panoramic view of recent advances in this field of research. Causality will be of interest to students and professionals in a wide variety of fields. Dr Judea Pearl has received the 2011 Rumelhart Prize for his leading research in Artificial Intelligence (AI) and systems from The Cognitive Science Society.

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.
Clarifies both traditional methods and current, popular algorithms (e.g. neural nets, random forests)
Written by two world-leading researchers
Addressed to all fields that work with data

本书作为实验设计与分析领域的名著, 是作者在亚利桑那州立大学、华盛顿大学和佐治亚理工学院三所大学近40年实验设计教学经验的基础上编写的. 全书内容广泛, 实例丰富,包括简单比较试验、析因设计、分式析因第1章设计、拟合回归模型、响应曲面方法和设计、稳健参数设计和过程稳健性研究、含随机因子的实验、嵌套设计和裂区设计等.
本书可作为自然科学研究人员、工程技术人员、管理人员进行科学实验设计与分析的参考书, 也可作为农林类、医学类、生物类、统计类的教师和高年级本科生和研究生的教学参考用书.

时间序列分析是用随机过程理论和数理统计学的方法，研究随机数据序列所遵从的统计规律，用于解决科研、工程技术、金融及经济等诸多领域内的实际问题。本书是一本由浅入深的小波分析导论，介绍了基于小波的时间序列统计分析。实践中的离散时间技术是本书的论述重点，同时对于理解和实现离散小波变换将涉及的诸多原理与算法也进行了详细的描述。
本书详细地介绍了小波方法在时间序列分析中的应用，图例丰富，语言简明易懂，论述严谨，另外，本书对小波分析所需要的数学知识进行了简洁实用的讲解，还在正文中嵌入了大量的练习，并在附录中给出了这些练习的答案，同时每章另备有适于课堂布置的练习。
本书适合作为高等院校统计学、数学等专业学生的教材，同时也可作为从事相关领域研究的人员的参考书。

《社会科学因果推断的理论基础》系统介绍了反事实的因果推论框架以及如何采用倾向值方法帮助社会科学经验研究者进行因果推论。除了基本的统计学原理之外，《社会科学因果推断的理论基础》回顾了倾向值方法的历史、发展及其对调查研究的意义，以及如何利用倾向值方法处理因果关系中的多类别性、中介性与异质性。除此之外，《社会科学因果推断的理论基础》还通过专门章节分析了比较个案研究中的综合控制个案方法以及因果推论过程中如何确定分析样本的样本量以及统计检定力。