《数字通信》(第4版)是数字通信领域的一本经典教材,通过对概率论及随机过程的复习,详细介绍了数字和模拟信源编码、数字调制信号和窄带信号与系统的特征、加性高斯白噪声中数字通信的调制和最佳调制与检测方法、基于最大似然准则的载波相位估计和定时同步的方法、不同信道模型的信道容量及随机编码、带限信道的信号设计、受到符号间干扰恶化信号的解调与检测问题、自适应信道均衡、多信道与多载波调制、扩展频谱信号和系统、衰落信道上的数字通信。

统计学的思想和各种统计数据对政府、社会乃至我们的工作和日常生活都有着不可忽视的影响，甚至超乎你的想象。通过阅读本书，你将会对我们生活的这个世界有更完整、更清晰的认识。 这不是一本讲述干巴巴的统计学理论的书，它主要介绍统计学概念的应用及其对日常生活、公共政策和许多其他领域的影响。书中没有繁琐的公式、图表和计算，你只要看得懂而且会解简单的方程式就足够了。本书的重点在于启发思考，这比生搬硬套地使用数学公式更有助于训练你的看问题的视角和解决问题的思维。本书把统计学概念分成4个部分来呈现：
数据的生产：数据的来源非常重要，这是统计学当中*影响力的概念之一；整合数据：即使用很简单的方法，也能很睿智地解读数据。机会与概率：用概率思维解决问题，可以把事实和无关紧要的干扰信息区分开。统计推断：用手中的少量数据，推断出关于一个较大的总体的研究结论。
本书一点儿也不枯燥乏味，恰恰相反，它是那样生动有趣，深入浅出地把统计学的概念和分析方法呈现在你面前。通过一个个具体的案例、简单的练习和知识普及，本书能让你在阅读过程中不知不觉地增长统计学知识，提高分析和解决问题的水平。这是一本能给你带来阅读乐趣的书，也是一本能让你更睿智的书。

Machine learning allows computational systems to adaptively improve their performance with experience accumulated from the observed data. Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.

《进化动力学:探索生命的方程》一书阐释了生命进化所遵循的数学原理。进化动力学主要涉及复制、突变、选择、随机漂变和空间运动等过程。《进化动力学:探索生命的方程》结合生物学理论和数学语言对这些问题进行了论述。开篇简要介绍了与进化相关的基本概念、种群动力学基本模型以及准种理论；然后介绍进化动力学基本研究方法及其应用，分别就合作行为、HIV、病原体、癌症以及人类语言的进化机制展开讨论，其中所涉及的研究方法主要有进化博弈理论、囚徒困境模型、进化图论、网络博弈等。
《进化动力学:探索生命的方程》语言简洁有力，论述生动有趣。虽然书中涉及大量的数学方法，但是，读者只需具备一定的数学基础，就不会感到晦涩枯燥。该书适合于具有生物学、数学以及具有其他相关学科背景的读者阅读。

This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms.

An essential guide to the trouble spots and oddities of R. In spite of the quirks exposed here, R is the best computing environment for most data analysis tasks. R is free, open-source, and has thousands of contributed packages. It is used in such diverse fields as ecology, finance, genomics and music. If you are using spreadsheets to understand data, switch to R. You will have safer -- and ultimately, more convenient -- computations.

Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work.
The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation.
By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling.

Walter Rudin's memoirs should prove to be a delightful read specifically to mathematicians, but also to historians who are interested in learning about his colorful history and ancestry. Characterized by his personal style of elegance, clarity, and brevity, Rudin presents in the first part of the book his early memories about his family history, his boyhood in Vienna throughout the 1920s and 1930s, and his experiences during World War II.
Part II offers samples of his work, in which he relates where problems came from, what their solutions led to, and who else was involved. As those who are familiar with Rudin's writing will recognize, he brings to this book the same care, depth, and originality that is the hallmark of his work.

《概率(第2卷)(修订和补充第3版)》是俄国著名数学家A.H.施利亚耶夫的力作。施利亚耶夫是现代概率论奠基人、前苏联科学院院士、著名数学家A.H.柯尔莫戈洛夫的学生，在概率统计界和金融数学界影响极大。《概率(第2卷)(修订和补充第3版)》作为莫斯科大学最为出色的概率教材之一。分为一、二两卷，并配有习题集。第二卷《概率(第2卷)(修订和补充第3版)》是离散时间随机过程（随机序列）的内容。重点讲述（强和弱）平稳序列、鞅和马尔可夫链，并给出了随机序列中的估计和过滤问题、随机金融数学、保险理论和最优停时问题等领域的应用。书后附有概率的数学理论形成的简史。在图书文献资料中，指出了所引用结果的出处，并且给出了注释。此外，还列出了相应的补充文献资料。第一卷《概率(第2卷)(修订和补充第3版)》是初等概率论的内容，可以作为初步了解概率论学科的教材。大部分内容涉及以柯尔莫戈洛夫公理化体系为基础的初等概率论、概率论的数学基础、概率测度的收敛性和极限定理等基本问题。

Douglas Hofstadter's long-awaited return to the themes of Gödel, Escher, Bach--an original and controversial view of the nature of consciousness and identity.
Can thought arise out of matter? Can self, a soul, a consciousness, an "I" arise out of mere matter? If it cannot, then how can you or I be here?
I Am a Strange Loop argues that the key to understanding selves and consciousness is the "strange loop"--a special kind of abstract feedback loop inhabiting our brains. The most central and complex symbol in your brain or mine is the one called "I." The "I" is the nexus in our brain, one of many symbols seeming to have free will and to have gained the paradoxical ability to push particles around, rather than the reverse.
How can a mysterious abstraction be real--or is our "I" merely a convenient fiction? Does an "I" exert genuine power over the particles in our brain, or is it helplessly pushed around by the laws of physics?
These are the mysteries tackled in I Am a Strange Loop, Douglas R. Hofstadter's first book-length journey into philosophy since Gödel, Escher, Bach. Compulsively readable and endlessly thought-provoking, this is the book Hofstadter's many readers have been waiting for.

该书集中反映了美国埃里克森儿童发展研究生院团队长达七年的研究成果。该团队发展了一组幼儿数学核心概念，并以此依据指导了多种类型的幼儿教师培训。七年来，该中心以幼儿数学核心概念为指导的幼儿教师培训，有效地提高了幼儿教师的数学教学和儿童的学习成绩。

《伽罗瓦理论》内容简介：This exposition of Galois theory was originally going to be Chapter 1 of thecontinuation of my book Fermat's Last Theorem, but it soon outgrew anyreasonable bounds for an introductory chapter, and I decided to make it aseparate book. However, this decision was prompted by more than just thelength. Following the precepts of my sermon "Read the Masters！" [E2], Imade the reading of Galois' original memoir a major part of my study ofGalois theory, and I saw that the modern treatments of Galois theory lackedmuch of the simplicity and clarity of the original. Therefore I wanted towrite about the theory in a way that would not only explain it, but explain itin terms close enough to Galois' own to make his memoir accessible to thereader, in the same way that I tried to make Riemann's memoir on the zetafunction and Kummer's papers on Fermat's Last Theorem accessible in myearlier books, [El] and [E3]. Clearly I could not do this within the confinesof one expository chapter.

《代数特征值问题》是一本计算数学名著。作者用摄动理论和向后误差分析方法系统地论述代数特征值问题以及有关的线性代数方程组、多项式零点的各种解法，并对方法的性质作了透彻的分析。《代数特征值问题》的内容为研究代数特征值及有关问题提供了严密的理论基础和强有力的工具。《代数特征值问题》共分九章。第一章叙述矩阵理论，第二、三章介绍摄动理论和向后舍入误差分析方法，第四章分析线性代数方程组解法，第五章讨论Hermite矩阵的特征值问题，第六、七章研究如何把一般矩阵化为压缩型矩阵及压缩型矩阵的特征值的问题，第八章论述LR和QR算法，最后一章讨论各种迭代法。

《数学名著译丛•常微分方程》用现代数学观点阐述常微分方程论中的一些基本问题，《数学名著译丛•常微分方程》共分五章：基本概念，基本理论，线性系统，基本定理的证明和流形上的微分方程，《数学名著译丛•常微分方程》特点是注重几何和定性的考察，并且特别强调在力学中的应用。《数学名著译丛•常微分方程》论述严谨，深入浅出，并有大量图形、例题和问题，书后附有典型练习题，有助于读者深入理解《数学名著译丛•常微分方程》的内容。
《数学名著译丛•常微分方程》可供大学数学系高年级学生、研究生、教师及其他数学工作者参考。

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本书是Л.C庞特里亚金院士根据他多年在莫斯科大学数学力学系所用的讲义编成的一本教材。它的第一次出版是在1961年，现在的第6版有不少的修改。本书从编写的指导思想到内容的具体安排上，与传统教材有很大的不同。作者从常微分方程在现代科学技术方面的应用出发，对材料作了新的选择和安排，不仅讲述了纯数学的常微分方程理论，同时还讲述了有关的技术应用本身。全书包括引论，常系数线性方程，变系数线性方程，存在性定理，稳定性共五章，另外还有两个与本书内容密切联系的附录，即一些分析问题和线性代数知识。每节后面都有例子或者实际应用问题。.
本书可供高等学校数学、物理、工程及相关专业的本科生、硕士生、教师，以及相关领域的研究人员参考使用。...

In most mathematics departments at major universities one of the three or four basic first-year graduate courses is in the subject of algebraic topology. This introductory textbook in algebraic topology is suitable for use in a course or for self-study, featuring broad coverage of the subject and a readable exposition, with many examples and exercises. The four main chapters present the basic material of the subject: fundamental group and covering spaces, homology and cohomology, higher homotopy groups, and homotopy theory generally. The author emphasizes the geometric aspects of the subject, which helps students gain intuition. A unique feature of the book is the inclusion of many optional topics which are not usually part of a first course due to time constraints, and for which elementary expositions are sometimes hard to find. Among these are: Bockstein and transfer homomorphisms, direct and inverse limits, H-spaces and Hopf algebras, the Brown representability theorem, the James reduced product, the Dold-Thom theorem, and a full exposition of Steenrod squares and powers. Researchers will also welcome this aspect of the book.