统计学完全教程
All of Statistics
Larry Wasserman · 初版 2004 年
40人推荐
60位读者提及
70次提及
为什么值得读
- 内容更偏向机器学习应用场景
- 兼具扎实且传统的数理推导
- 非常受计算机科学背景读者欢迎
“本书面向希望快速掌握 probability 和 statistics 的读者。它将现代 statistics 的诸多核心思想汇集一处。本书适合 statistics、computer science、data mining 和 machine learning 领域的学生及研究人员。”“与典型的 mathematical statistics 入门教材相比，本书涵盖的主题要宽广得多。它包含了 nonparametric curve estimation、bootstrapping 和 classification 等通常被划归后续课程的现代主题。读者只需具备 calculus 和少许 linear algebra 基础
Hacker News 上怎么说
正在加载完整评论……
……倒不如先从更入门的书读起，把基础彻底搞懂。比如 Blitzstein 和 Hwang 的《Introduction to Probability》，然后对于需要学更多统计的人，可以读 McElreath 的《Statistical Rethinking》或者 Wasserman 的《All of Statistics》。至于如何培养良好的软件判断力和习惯，我都不知道该推荐什么好了。这方面似乎真没有什么捷径。也许可以看看《Fluent Python》或者《Effective……
…better served going with more introductory texts and really understanding them. Maybe Blitzstein and Hwang's "Introduction to Probability" and then McElreath's "Statistical Rethinking" or Wasserman's "All of Statistics" for people who need more stats. I'm not even sure what to recommend for developing good software judgment and habits. There doesn't seem to be a shortcut for that. Maybe "Fluent Python" or "Effective…
如果你懂微积分和线性代数，又想学统计学，我首推 Larry Wasserman 写的《All of Statistics》。
All of Statistics by Larry Wasserman is the book I recommend for anyone who knows calculus and linear algebra and wants to learn statistics.
……Stein 和 Shakarchi 合著的 4 本涵盖实分析、复分析和泛函分析的书，应该能满足你的需求。应用数学方面：3) 概率论——Grinstead 和 Snell 的、Durrett 的书都很不错。4) Larry Wasserman 的《All of Statistics》，更偏向 ML 一些。5) Boyd 和 Vanderberghe 的《Optimization》。7) 并没有单独一个叫“计算机科学”的主题，但顺着你关注的那些主题来看……
…4 books covering real, complex and functional analysis by Stein and Shakarchi, which should serve your purpose Applied Math : 3) Probability - Grinstead and Snell, Durrett both have good books. 4) All of Statistics by Larry Wasserman for a more ML bent to it 5) Optimization by Boyd and Vanderberghe 7) There's no single topic called "Computer Science", but going with the theme of the topics you are looking at,…
……Machine Learning (https://smile.amazon.com/Pattern-Recognition-Learning-Inform...) * Machine Learning: A Probabilistic Perspective (https://smile.amazon.com/Machine-Learning-Probabilistic-Pers...) * All of Statistics: A Concise Course in Statistical Inference (https://smile.amazon.com/gp/product/0387402721/) * Elements of Statistical Learning: Data Mining, Inference, and Prediction (https://smile.amazon.com/gp/product/0387848576(……
…Machine Learning (https://smile.amazon.com/Pattern-Recognition-Learning-Inform...) * Machine Learning: A Probabilistic Perspective (https://smile.amazon.com/Machine-Learning-Probabilistic-Pers...) * All of Statistics: A Concise Course in Statistical Inference (https://smile.amazon.com/gp/product/0387402721/) * Elements of Statistical Learning: Data Mining, Inference, and Prediction (https://smile.amazon.com/gp/product/0387848576(…
Larry Wasserman 的《All of Statistics》写得非常好。它属于传统的、偏重数学推导的统计学，但受众定位比较普适。（在计算机科学圈子里尤其受欢迎。）
"All of Statistics" by Larry Wasserman is very good. It is traditional, fairly mathematical statistics, but intended for a general audience. (It is particularly popular with computer science people.)
我个人不太喜欢分位数 bootstrap（percentile bootstrap），但那份参考资料对待本应更严谨的内容显得有点太草率了。Wasserman 在《All of Statistics》第 8 章中就严谨得多，详细列出了 percentile bootstrap 适用的条件。不仅如此，他还通过一个具体例子论证了 percentile bootstrap 确实……
I'm not a big fan of the percentile bootstrap, but that reference is a little too cavalier with material that deserves to be treated more rigorously. Chapter 8 of Wasserman's "All of Statistics" is much more careful about outlining the conditions under which the percentile bootstrap will work. Moreover, he works through a specific example that demonstrates that the percentile bootstrap does…