作者:
Sharon Bertsch McGrayne 出版社: Yale University Press 副标题: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy 出版年: 2011-5-17 页数: 336 定价: USD 27.50 装帧: Hardcover ISBN: 9780300169690
Drawing on primary source material and interviews with statisticians and other scientists, "The Theory That Would Not Die" is the riveting account of how a seemingly simple theorem ignited one of the greatest scientific controversies of all time. Bayes' rule appears to be a straightforward, one-line theorem: by updating our initial beliefs with objective new information, we get...
Drawing on primary source material and interviews with statisticians and other scientists, "The Theory That Would Not Die" is the riveting account of how a seemingly simple theorem ignited one of the greatest scientific controversies of all time. Bayes' rule appears to be a straightforward, one-line theorem: by updating our initial beliefs with objective new information, we get a new and improved belief. To its adherents, it is an elegant statement about learning from experience. To its opponents, it is subjectivity run amok. In the first-ever account of Bayes' rule for general readers, Sharon Bertsch McGrayne explores this controversial theorem and the human obsessions surrounding it. She traces its discovery by an amateur mathematician in the 1740s through its development into roughly its modern form by French scientist Pierre Simon Laplace. She reveals why respected statisticians rendered it professionally taboo for 150 years - at the same time that practitioners relied on it to solve crises involving great uncertainty and scanty information, even breaking Germany's Enigma code during World War II, and explains how the advent of off-the-shelf computer technology in the 1980s proved to be a game-changer. Today, Bayes' rule is used everywhere from DNA decoding to Homeland Security. "The Theory That Would Not Die" is a vivid account of the generations-long dispute over one of the greatest breakthroughs in the history of applied mathematics and statistics.
作者简介
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Sharon Bertsch McGrayne is the author of numerous books, including Nobel Prize Women in Science: Their Lives, Struggles, and Momentous Discoveries and Prometheans in the Lab: Chemistry and the Making of the Modern World. She lives in Seattle.
原文摘录
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Savage was confronting the thorniest objection to Bayesian methods: “If prior opinions can differ from one researcher to the next, what happens to scientific objectivity in data analysis?”17 Elaborating on Jeffreys, Savage answered as follows: as the amount of data increases, subjectivists move into agreement, the way scientists come to a consensus as evidence accumulates about, say, the greenhouse effect or about cigarettes being the leading cause of lung cancer. When they have little data, scientists disagree and are subjectivists; when they have piles of data, they agree and become objectivists. Lindley agreed: “That’s the way science is done.” (查看原文)
As far as Savage was concerned, Bayes’ rule filled a need that other statistical procedures could not. Frequentism’s origin in genetics and biology meant it was involved with group phenomena, populations, and large aggregations of similar objects. As for using statistical methods in biology or physics, the Nobel Prize–winning physicist Erwin Schrödinger said, “The individual case [is] entirely devoid of interest.”22 Bayesians like Savage, though, could work with isolated one-time events, such as the probability that a chair weighs 20 pounds, that a plane would be late, or that the United States would be at war in five years. (查看原文)
这本书的作者不是数学家，因此有些问题讲得不是很透彻，我甚至怀疑某些地方存在错误。例如她说算后验概率时要进行迭代，即将算出来的结果作为先验概念再与likelyhood function相乘，Pirre Simon Laplace是这样做的，而Data Analysis A Bayesian Tutorial在2.1节的末尾明确说这是一种Boot-strip的方法，只能自欺欺人。
1 有用 喂虾米 2021-12-20 14:09:33
在贝叶斯最低谷的时候，军方和保险业依然在使用它，这就是搞实际问题和搞学术理论的区别～
2 有用 紧握刀锋 2018-01-13 11:21:17
作为一本历史书，我觉得还挺好的，各种小故事，很适合我这种学术八卦爱好者，虽然有些素材跟其他书是重复的。如果把这个当科普看，那就要失望了，一个讲数学应用的科普没有公式，这能想象么。看了下作者Sharon McGrayne的个人网站，Swarthmore College的B. A.出身，所以尽管这位阿姨写了好些关于科学家的书，也不停在学术会议上作报告（我觉得底下听的人感觉应该像我们听外国人说相声），但... 作为一本历史书，我觉得还挺好的，各种小故事，很适合我这种学术八卦爱好者，虽然有些素材跟其他书是重复的。如果把这个当科普看，那就要失望了，一个讲数学应用的科普没有公式，这能想象么。看了下作者Sharon McGrayne的个人网站，Swarthmore College的B. A.出身，所以尽管这位阿姨写了好些关于科学家的书，也不停在学术会议上作报告（我觉得底下听的人感觉应该像我们听外国人说相声），但是我觉得她应该不懂，所以也不能苛求。 (展开)
0 有用 sequoia 2012-04-09 22:13:39
这本书的作者不是数学家，因此有些问题讲得不是很透彻，我甚至怀疑某些地方存在错误。例如她说算后验概率时要进行迭代，即将算出来的结果作为先验概念再与likelyhood function相乘，Pirre Simon Laplace是这样做的，而Data Analysis A Bayesian Tutorial在2.1节的末尾明确说这是一种Boot-strip的方法，只能自欺欺人。
0 有用 Fantasy 2016-08-29 09:14:06
拉普拉斯粉乱入试图分裂贝叶斯朋友圈，果然是人心可谓。
0 有用 J.X 2013-12-29 21:42:53
Bayes
0 有用 纯粹的主观评价 2023-12-10 23:22:04 广东
对贝叶斯主意产生了兴趣，这本书提供了一系列关于其的历史故事
0 有用 小费下次问我要 2023-07-31 13:18:56 上海
当我浏览书页时多么想记起贝叶斯公式可我记不得，如果放在六年前阅读我会怀揣着更加敬畏的心吧；越是深入，我就越感到不安：曾经耳熟能详的机器学习原理终也没能掌握
0 有用 o` 2022-10-31 11:38:35 加拿大
对我来说，用三百多页的篇幅去讲贝叶斯理论在历史中的沉浮实在是太无聊了……如果你的预期是从中读到贝叶斯在各种场景下的应用，你会很失望。
0 有用 AssertionError 2022-03-31 18:09:53
从同学家偷的，以为是methodology/causal inference，发现本质是很浅的科普……作为流行科普写得也还行吧。
1 有用 喂虾米 2021-12-20 14:09:33
在贝叶斯最低谷的时候，军方和保险业依然在使用它，这就是搞实际问题和搞学术理论的区别～