An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

本书是当代国际最著名的统计学家之一C.R.劳的一部统计学哲理论著，也是他毕生统计学术思想的总结，同时还是一本通俗的关于统计学原理的普及教科书。在本书中，作者从哲学的角度论述了统计学原理，通过实例，不仅证明了统计学是一门最严格、最合理的认识论和方法学 ，还深刻地揭示了现代统计学发展的过程，特别是那些很深刻的理论是如何从一些非常简单实际的问题中发展起来的。
本书前5章讲述了统计学从最初收集、汇编数据为行政管理服务，发展成为有一整套原理和研究方法的独立学科的历史，第6章谈及了普通公众对统计学的理解，强调了从数字中学习有助于成为有效率的公民。
最引人注目的特点是，书中提到的所有科学的学科调查与决策和统计之间的关联，是由一系列实例来说明的。本书使用非专业语言通俗地阐述了统计学的基本概念和方法，适合大众读者。

The standard rules of probability can be interpreted as uniquely valid principles in logic. In this book, E. T. Jaynes dispels the imaginary distinction between 'probability theory' and 'statistical inference', leaving a logical unity and simplicity, which provides greater technical power and flexibility in applications. This book goes beyond the conventional mathematics of probability theory, viewing the subject in a wider context. New results are discussed, along with applications of probability theory to a wide variety of problems in physics, mathematics, economics, chemistry and biology. It contains many exercises and problems, and is suitable for use as a textbook on graduate level courses involving data analysis. The material is aimed at readers who are already familiar with applied mathematics at an advanced undergraduate level or higher. The book will be of interest to scientists working in any area where inference from incomplete information is necessary.

我们在生活在一个充满不确定性的世界，从买彩票的运气到股市的波动，从高尔夫球进洞的曲线到明天究竟会不会下雨，如果一本畅销书或一部卖座的电影可以被预测，那么《哈利·波特》为什么会被拒稿9次？如果成功不可以被复制，那么很多连锁企业又是如何获得成功的？
《醉汉的脚步》来自一个描述随机运动的数学术语，当分子飞越空间并不断撞击其他分子或被其他分子撞击时，它走过的路径就如“醉汉的脚步”一样。我们可以用分子的路径来比拟我们的生活，或是我们从大学到工作、从单身到建立家庭、打高尔夫球时从进第1洞到进第18洞之间的过程。作者列纳德·蒙洛迪诺在为我们揭示偶然性的真实本性以及导致我们误判周遭世界的那些心理错觉的同时，也为我们提供一种看待生活的全新视角，帮助我们更智慧、深刻地认识世界，理解生活。

为什么有些酒吧这个礼拜人潮涌动，下个礼拜却空空荡荡？为什么《隐藏的逻辑》能在畅销榜单上久居不下？为什么邻居一夜之间成了暴发户？为什么股市会起伏波动？要找到这些问题的答案，就要像物理学家研究原子一样来研究人类社会，要思考的是模式，而不是人。多少年来，人类做决策的特性把大多数的经济学家和社会理论家都搞糊涂了，他们依赖一种老式的思维方式，认为人类社会之所以复杂是因为人是复杂的。现在，理论物理学家马克·布坎南（Mark Buchanan）告诉我们，在人类社会正在上演一场“量子革命”。物理学法则开始为我们描绘出一幅有关人或“社会原子”的崭新图像，而且与现实存在的个体自由意志毫不冲突。混乱的原子活动能够组合成精准的热力学，人类的自由个体也同样能组合成可预测的模式。社会物理学家能剖析潮流的变化；能预测企业是成是败：能解释犯罪增多的原因。布坎南在这本开阔眼界的书里提出，了解群体组织的规律是我们这个时代面临的主要挑战。《隐藏的逻辑》例证丰富，论点尖锐，容易理解，充满了智趣的游戏和刺激的实验，为我们看待人的社会行为提供了一个全新的视角。

《边缘奇迹:相变和临界现象》通过对相变和临界现象的介绍，阐述热力学和统计物理的基本概念，从熵的引入、统计配分函数，到对称破缺、标度律和普适性。《边缘奇迹:相变和临界现象》也描述了研究相变现象的基本理论方法，包括平均场近似、标度分析、重正化群、统计模型精确解和计算机数值模拟等。书中还介绍了相变研究的最新进展，如有限系统的临界现象和量子相变。相变和临界现象是物理学中充满难题和意外发现的领域之一。精确的数字语言，使物理学上升为一种艺术。只有下功夫掌握数学语言的人，将来才可能在深入钻研之后享受这种艺术之美。千姿百态的“水”、“有没有永久的气体？”、四维以上空间才正确的理论、非平衡相变——自然界中的有序和混沌……
0℃时冰溶化成水，100℃时水沸腾成蒸气，这种现象司空见惯。仔细想想，为什么这10 23个水分子，单个水分子结构不变，相互作用不变，会“集体地”、“不约而同地”从一个相“变”到另一个相呢？“新相”在“老相”中又如何“孕育”、“形成”？

《溯源探幽：熵的世界》着重论述熵的基本概念，分别从热力学、统计物理、分子动理论、信息论、非线性动力学、天体物理和宇宙论等不同侧面、不同层次来剖析其蕴含的意义。同时，在宽广的范围内讨论了熵在现代自然科学与技术中的应用，阐明了它所处的地位。《溯源探幽：熵的世界》内容丰富、取材新颖、文笔生动、通俗易懂，可供高等院校学生、中学教师、科技工作者以及科学爱好者阅读。

This third edition of a classic textbook presents a comprehensive introduction to Bayesian data analysis. Written for students and researchers alike, the text is written in an easily accessible manner with chapters that contain many exercises as well as detailed worked examples taken from various disciplines. This third edition provides two new chapters on Bayesian nonparametrics and covers computation systems BUGS and R. It also offers enhanced computing advice. The book's website includes solutions to the problems, data sets, software advice, and other ancillary material.

因为精确预测以及在科技领域的广泛应用，量子力学被认为是最成功的科学理论之一，但也是最被误解的理论之一。在被创立后的近一个世纪，量子力学仍旧充满了争议。通过量子贝叶斯理论（QBism）解释量子理论中的悖论和谜题，本书为非专业的读者阐述了量子力学深远的含义、如何理解量子力学和量子力学如何与这个世界相互作用。QBism用对概率的全新理解去改造量子力学中的传统特征。贝叶斯概率与标准的“频率概率”不同的是，它是观察者对未来将要发生的一个事件或者一个命题的信任程度的数值测量。相比于频率主义，量子贝叶斯理论的优势在于它能够处理单个事件，它的概率估计可以根据获得的新信息去更新，并且可以包含“频率概率”的结果。但最重要的还是与量子理论相关的奇怪之处——如两个原子可以同时在不同的位置，信号可以传播得比光更快，以及薛定谔的猫可以同时处于死和活的状态的想法。
用直白的语言而不是方程，贝耶尔用一种通俗的方式，揭示了量子力学的意义，发现了认识物理学的新途径。

Unlike most other texts on the subject, this clear, concise introduction to the theory of microscopic bodies treats the modern theory of critical phenomena. Provides up-to-date coverage of recent major advances, including a self-contained description of thermodynamics and the classical kinetic theory of gases, interesting applications such as superfluids and the quantum Hall effect, several current research applications, The last three chapters are devoted to the Landau-Wilson approach to critical phenomena. Many new problems and illustrations have been added to this edition.

《理论物理学教程(第5卷):统计物理学1(第5版)》是根据俄文最新版译出。《理论物理学教程(第5卷):统计物理学1(第5版)》以吉布斯方法为基础讲述统计物理学。全书论述热力学基础，理想气体，非理想气体理论，费米分布与玻色分布，固体统计理论，溶液理论，化学反应与表面现象，高密度下物质的性质，晶体的对称性，涨落理论，相平衡、二级相变和临界现象。《理论物理学教程(第5卷):统计物理学1(第5版)》可作为高等学校物理专业高年级本科生或研究生的教学参考书，也可供相关专业的研究生、科研人员和教师参考。
《理论物理学教程》（共十卷）是一部享誉世界的理论物理学巨著，是反映经典物理学向现代物理学转变的里程碑式的重要著作，于1962年获得列宁奖。原著为俄文，现已有十余种文字的分卷译本，六种文字的全卷译本。本教程中的七卷是由诺贝尔物理学奖获得者、苏联科学院院士、伟大的理论物理学家л.д.朗道和他的学生、苏联科学院院士、杰出的理论物理学家е.м.栗弗席兹在20世纪40－50年代陆续编写而成的，另外三卷由栗弗席兹和俄罗斯科学院院士л.п.皮塔耶夫斯基等人按朗道的计划在20世纪60－70年代编写完成，后经不断补充完善，现已成为举世公认的经典学术著作。本套教程内容丰富、立论明确、论证严谨、物理图像清晰，涵盖了理论物理学从微观到宏观的各个领域，各卷中附有丰富的习题及解答，是学习理论物理学的必备参考书。

这是一本具有李政道特色的统计力学专著。它凝聚了李先生在统计力学方面的治学结晶，自成体系。虽只有短短的四章，却几乎概括了统计力学的所有精髓。读者可以使用本书作为学习统计力学的入门教材，也可以在使用别的教材时用它作为学习的参考和补充材料。李政道先生治学严谨，一切推导都从最基本假定出发的研究风格，在本书中得到充分的体现。

《热力学·统计物理（第五版）》是“十二五”普通高等教育本科*规划教材，是作者在第四版的基础上全面修订而成的。本次修订保持了原书叙述简明、便于教学的特点，在此基础上增删和改写了部分章节，对某些问题的讲述作了修改和补充，使《热力学·统计物理（第五版）》的内容更加充实、新颖，科学性、先进性和教学适用性有了进一步的增强。
《热力学·统计物理（第五版）》共11章，内容包括：热力学的基本规律，均匀物质的热力学性质，单元系的相变，多元系的复相平衡和化学平衡，不可逆过程热力学简介，近独立粒子的*概然分布，玻耳兹曼统计，玻色统计和费米统计，系综理论，涨落理论，非平衡态统计理论。
《热力学·统计物理（第五版）》可作为高等学校物理类各专业的教材，也可供其他有关人员参考。
十二五规划，十一五规划，国家教委优秀教材一等奖，国家教委科技进步二等奖

德国著名理论物理学家W．Griner教授编写了一套13卷集的理论物理学教科书, 本书是其中之一。这是一套内容完整而实用的大学生及硕士研究生现代物理学教材。它以系统、统一和连贯的方式阐述了现代理论物理学的诸方面。这套教材面世后，不仅在德国产生了巨大的影响，其英文版的及时推出，对全世界理论物理学的教学也起了很好的促进作用。

In each generation, scientists must redefine their fields: abstracting, simplifying and distilling the previous standard topics to make room for new advances and methods. Sethna's book takes this step for statistical mechanics - a field rooted in physics and chemistry whose ideas and methods are now central to information theory, complexity, and modern biology. Aimed at advanced undergraduates and early graduate students in all of these fields, Sethna limits his main presentation to the topics that future mathematicians and biologists, as well as physicists and chemists, will find fascinating and central to their work. The amazing breadth of the field is reflected in the author's large supply of carefully crafted exercises, each an introduction to a whole field of study: everything from chaos through information theory to life at the end of the universe.

What is deep learning for those who study physics? Is it completely different from physics? Or is it similar?
In recent years, machine learning, including deep learning, has begun to be used in various physics studies. Why is that? Is knowing physics useful in machine learning? Conversely, is knowing machine learning useful in physics?
This book is devoted to answers of these questions. Starting with basic ideas of physics, neural networks are derived naturally. And you can learn the concepts of deep learning through the words of physics.
In fact, the foundation of machine learning can be attributed to physical concepts. Hamiltonians that determine physical systems characterize various machine learning structures. Statistical physics given by Hamiltonians defines machine learning by neural networks. Furthermore, solving inverse problems in physics through machine learning and generalization essentially provides progress and even revolutions in physics. For these reasons, in recent years interdisciplinary research in machine learning and physics has been expanding dramatically.
This book is written for anyone who wants to learn, understand, and apply the relationship between deep learning/machine learning and physics. All that is needed to read this book are the basic concepts in physics: energy and Hamiltonians. The concepts of statistical mechanics and the bracket notation of quantum mechanics, which are explained in columns, are used to explain deep learning frameworks.
We encourage you to explore this new active field of machine learning and physics, with this book as a map of the continent to be explored.

"A remarkable book that influenced the scientific thought of an entire generation."--Dictionary of Scientific Biography
A major statement of the language, method, and concepts of the physical sciences, this 1892 volume traces not only the history of experimental investigation but also the efforts of philosophic minds to state and organize their findings intelligently. A classic in the philosophy of science, its author is the founder of modern statistics. Karl Pearson was among the most influential university teachers of his era, and he possessed a remarkable ability to captivate both students and casual listeners. In The Grammar of Science, his most widely read book, he introduced the concept of a general methodology underlying all science, and thus made one of the great contributions to modern thought. 1957 ed.

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'.