Book Description
Describes the theoretical background behind Statistical Parametric Mapping and provides operational guidelines and technical details on data analysis.
Product Description
In an age where the amount of data collected from brain imaging is increasing constantly, it is of critical importance to analyse those data within an accepted framework to ensure proper integration and comparison of the information collected. This book describes the ideas and procedures that underlie the analysis of signals produced by the brain. The aim is to understand how the brain works, in terms of its functional architecture and dynamics. This book provides the background and methodology for the analysis of all types of brain imaging data, from functional magnetic resonance imaging to magnetoencephalography. Critically, Statistical Parametric Mapping provides a widely accepted conceptual framework which allows treatment of all these different modalities. This rests on an understanding of the brain's functional anatomy and the way that measured signals are caused experimentally. The book takes the reader from the basic concepts underlying the analysis of neuroimaging data to cutting edge approaches that would be difficult to find in any other source. Critically, the material is presented in an incremental way so that the reader can understand the precedents for each new development. This book will be particularly useful to neuroscientists engaged in any form of brain mapping; who have to contend with the real-world problems of data analysis and understanding the techniques they are using. It is primarily a scientific treatment and a didactic introduction to the analysis of brain imaging data. It can be used as both a textbook for students and scientists starting to use the techniques, as well as a reference for practicing neuroscientists. The book also serves as a companion to the software packages that have been developed for brain imaging data analysis.
* An essential reference and companion for users of the SPM software
* Provides a complete description of the concepts and procedures entailed by the analysis of brain images
* Offers full didactic treatment of the basic mathematics behind the analysis of brain imaging data
* Stands as a compendium of all the advances in neuroimaging data analysis over the past decade
* Adopts an easy to understand and incremental approach that takes the reader from basic statistics to state of the art approaches such as Variational Bayes
* Structured treatment of data analysis issues that links different modalities and models
* Includes a series of appendices and tutorial-style chapters that makes even the most sophisticated approaches accessible
From the Back Cover
In an age where the amount of data collected from brain imaging is increasing constantly, it is of critical importance to analyse those data within an accepted framework to ensure proper integration and comparison of the information collected. This book describes the ideas and procedures that underlie the analysis of signals produced by the brain. The aim is to understand how the brain works, in terms of its functional architecture and dynamics. This book provides the background and methodology for the analysis of all types of brain imaging data, from functional magnetic resonance imaging to magnetoencephalography. Critically, Statistical Parametric Mapping provides a widely accepted conceptual framework which allows treatment of all these different modalities. This rests on an understanding of the brain's functional anatomy and the way that measured signals are caused experimentally. The book takes the reader from the basic concepts underlying the analysis of neuroimaging data to cutting edge approaches that would be difficult to find in any other source. Critically, the material is presented in an incremental way so that the reader can understand the precedents for each new development. This book will be particularly useful to neuroscientists engaged in any form of brain mapping; who have to contend with the real-world problems of data analysis and understanding the techniques they are using. It is primarily a scientific treatment and a didactic introduction to the analysis of brain imaging data. It can be used as both a textbook for students and scientists starting to use the techniques, as well as a reference for practicing neuroscientists. The book also serves as a companion to the software packages that have been developed for brain imaging data analysis.
Key Features:
* An essential reference and companion for users of the SPM software
* Provides a complete description of the concepts and procedures entailed by the analysis of brain images
* Offers full didactic treatment of the basic mathematics behind the analysis of brain imaging data
* Stands as a compendium of all the advances in neuroimaging data analysis over the past decade
* Adopts an easy to understand and incremental approach that takes the reader from basic statistics to state of the art approaches such as Variational Bayes
* Structured treatment of data analysis issues that links different modalities and models
* Includes a series of appendices and tutorial-style chapters that makes even the most sophisticated approaches accessible

《随机微分方程》(第6版)是《Universitext》丛书之一，是一部理想的研究生教材，内容做了较大的修改和补充，包括鞅表示论、变分不等式和随机控制等内容，书后附有部分习题解答和提示。随机微分方程在数学以外的许多领域有着广泛的应用，它对数学领域中的许多分支起着有效的联结作用。

《概率、随机变量与随机过程》是美国著名学者A·帕普里斯教授所著的一本经典教材。自1965年第1版问世以来至今已第4版，一直被美国多所大学用作相关专业的研究生教材。它的特点是将高深的理论恰当地应用于工程实际，因而深受工程界专业人士的青睐。本书（第4版）在保持前三版风格和精华的基础上作了大量的修订：更新了约三分之一的章节内容，包括几个新的专题和新增的第15、16章，增加了大量的新例子，进一步澄清了一些复杂的概念，使读者能更容易地理解它们。
本书可供无线电通信系统、信号处理、控制理论、优化、滤波等专业的研究生和本科高年级学生使用，也可供相关领域的科开人员和工程技术人员参考。

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。

This book is an introduction to the field of asymptotic statistics. The treatment is both practical and mathematically rigorous. In addition to most of the standard topics of an asymptotics course, including likelihood inference, M-estimation, the theory of asymptotic efficiency, U-statistics, and rank procedures, the book also presents recent research topics such as semiparametric models, the bootstrap, and empirical processes and their applications. The topics are organized from the central idea of approximation by limit experiments, which gives the book one of its unifying themes. This entails mainly the local approximation of the classical i.i.d. set up with smooth parameters by location experiments involving a single, normally distributed observation. Thus, even the standard subjects of asymptotic statistics are presented in a novel way. Suitable as a graduate or Master's level statistics text, this book will also give researchers an overview of research in asymptotic statistics.

中国统计出版社

概率图模型将概率论与图论相结合，是当前非常热门的一个机器学习研究方向。本书详细论述了有向图模型（又称贝叶斯网）和无向图模型（又称马尔可夫网）的表示、推理和学习问题，全面总结了人工智能这一前沿研究领域的最新进展。为了便于读者理解，书中包含了大量的定义、定理、证明、算法及其伪代码，穿插了大量的辅助材料，如示例（examples）、技巧专栏（skill boxes）、实例专栏（case study boxes）、概念专栏（concept boxes）等。另外，在第 2章介绍了概率论和图论的核心知识，在附录中介绍了信息论、算法复杂性、组合优化等补充材料，为学习和运用概率图模型提供了完备的基础。
本书可作为高等学校和科研单位从事人工智能、机器学习、模式识别、信号处理等方向的学生、教师和研究人员的教材和参考书。
== 序 言 ==
很高兴能够看到我们所著的《概率图模型》一书被翻译为中文出版。我们了解到这本书涵盖的课题已在中国引起了巨大的兴趣。已有众多中国读者写信向我们解释这本书对于他们的学习的重要性，并希望获得更易理解的版本。随着众多来自中国研究机构或国外研究机构的中国学者署名或共同署名的文章的发表，中国研究者已在概率图领域中扮演了非常重要的角色。这些文章对于概率图模型领域的发展起到了非常重要的作用。我们相信《概率图模型》中文版的出版将帮助许多中国读者学习并掌握这一重要课题的基础。同时，这也将进一步提高中国学者应用概率图模型思想的能力，并为这一领域的发展做出贡献。
本书的翻译工作由王飞跃研究员主导，并得到了王珏研究员及其众多助手和合作者的支持。这是一份历时 5年、具有里程碑意义的努力，我深深地感谢该团队所有为本书翻译做出贡献的人员。我尤其希望借此机会感谢王珏研究员——一位中国机器学习领域的开拓者。王珏研究员是此项翻译工作的十分重要的推动者。没有他的支持，没有他的众多杰出的机器学习领域的学生的帮助，可能这项工作到现在还没有结果。很遗憾王珏研究员于 2014年 12月死于癌症，终年 66岁，已不能看到他努力的结果。然而，他的思想活在他的学生们的工作中，与本书的出版同在。
Daphne Koller
（复杂系统管理与控制国家重点实验室王晓翻译）

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

对于大多数心理学专业的学生而言，行为科学研究也许就意味着枯燥无味的数据收集、样本测量、资料的统计分析和纷繁实验方法的罗列体。然而事实真的是这样的吗？由美国最著名的统计学家弗雷德?格拉维特博士及其合作者罗妮安?佛泽诺博士根据多年教学经验共同编写的这本《行为科学研究方法》会向人们展示实验研究和非实验研究的迷人和魅力。通读全书，读者可以感受到作者不仅对行为科学研究过程非常熟练，而且他们谙熟教法，富于热情又细微周到，娓娓道来，宛如进行一次惬意的旅行，从研究的起点到论文的发表，中途偶有小憩，却是一气呵成！全书内容按照行为科学研究的实际过程来组织，将研究历程划分为九步，并对研究中的伦理学问题和资料分析进行了概括性地讨论。在这种充满盛情邀请和对话的氛围中，又提供了许多学习的辅助手段，使读者无法拒绝作者的热情，便随同他们一起去探索研究的历程-从起点一直到成果的发表，步骤分明而又环环相扣，作者强调了研究者在研究工作的每一步必须作出的决定，同时把研究方法和应用有机结合，不知不觉中把读者带入研究过程。全书根据研究的进程分为十五章。第一章、绪论：调研与科学方法；第二章、研究设想；第三章、研究中的伦理学；第四章、变量的界定与测量；第五章、研究被试的选择；第六章、研究方法和效度；第七章、描述法和相关法；第八章、实验研究方法；第九章、准实验研究方法；第十章、被试间实验设计；第十一章、被试内实验设计；第十二章、析因设计；第十三章、单被试研究设计；第十四章、数据的统计分析；第十五章、研究报告的撰写。
目录
“译丛”总序
译者序
前言
第一章引论：调研与科学方法
第一节研究方法学引论
第二节研究方法
第三节科学方法
第四节研究过程
本章小结
关键词
练习题
第二章研究设想
第一节研究的起步
第二节研究设想的来源
第三节搜索和利用背景文献
第四节进行文献检索
第五节寻找新的研究设想
第三章研究中的伦理学
第一节引论
第二节研究中的伦理问题和人类被试
第三节研究中的伦理问题和非人类被试
第四节科学研究的诚实和伦理问题
本章小结
关键词
练习题
第四章变量的界定与测量
第一节测量引论
第二节构念和操作定义
第三节测量的和信度
第四节测量量表
第五节测量的形式
第六节关于测量的其它问题
本章小结
关键词
练习题
第五章研究被试的选择
第六章研究方法和效度
第七章描述法和相关法
第八章实验研究方法
第九章准实验研究方法
第十章被试间实验设计
第十一章被试内实验设计
第十二章析因设计
第十三章单被试研究设计
第十四章数据的统计分析
第十五章研究报告的撰写
附录

Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies. They lay out the assumptions needed for causal inference and describe the leading analysis methods, including, matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher.

Celebrating the life of an admired pioneer in statistics In this captivating and inspiring memoir, world-renowned statistician George E. P. Box offers a firsthand account of his life and statistical work. Writing in an engaging, charming style, Dr. Box reveals the unlikely events that led him to a career in statistics, beginning with his job as a chemist conducting experiments for the British army during World War II. At this turning point in his life and career, Dr. Box taught himself the statistical methods necessary to analyze his own findings when there were no statisticians available to check his work. Throughout his autobiography, Dr. Box expertly weaves a personal and professional narrative to illustrate the effects his work had on his life and vice-versa. Interwoven between his research with time series analysis, experimental design, and the quality movement, Dr. Box recounts coming to the United States, his family life, and stories of the people who mean the most to him. This fascinating account balances the influence of both personal and professional relationships to demonstrate the extraordinary life of one of the greatest and most influential statisticians of our time. An Accidental Statistician also features: * Two forewords written by Dr. Box's former colleagues and closest confidants * Personal insights from more than a dozen statisticians on how Dr. Box has influenced and continues to touch their careers and lives * Numerous, previously unpublished photos from the author's personal collection An Accidental Statistician is a compelling read for statisticians in education or industry, mathematicians, engineers, and anyone interested in the life story of an influential intellectual who altered the world of modern statistics.

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。

《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。

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

Functional magnetic resonance imaging (fMRI) has become the most popular method for imaging brain function. Handbook of Functional MRI Data Analysis provides a comprehensive and practical introduction to the methods used for fMRI data analysis. Using minimal jargon, this book explains the concepts behind processing fMRI data, focusing on the techniques that are most commonly used in the field. This book provides background about the methods employed by common data analysis packages including FSL, SPM and AFNI. Some of the newest cutting-edge techniques, including pattern classification analysis, connectivity modeling and resting state network analysis, are also discussed. Readers of this book, whether newcomers to the field or experienced researchers, will obtain a deep and effective knowledge of how to employ fMRI analysis to ask scientific questions and become more sophisticated users of fMRI analysis software.

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

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.

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.