The Book of Why

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Judea Pearl is a professor of computer science at UCLA and winner of the 2011 Turing Award and the author of three classic technical books on causality. He lives in Los Angeles, California.

Dana Mackenzie is an award-winning science writer and the author of The Big Splat, or How Our Moon Came to Be. He lives in Santa Cruz, California.

出版者:Basic Books
作者:Judea Pearl
出品人:
页数:432
译者:
出版时间:2018-5-15
价格:USD 32.00
装帧:Hardcover
isbn号码:9780465097609
丛书系列:
图书标签:
  • 统计 
  • 逻辑 
  • 方法论 
  • 计算机 
  • 因果 
  • 哲学 
  • 科普 
  • AI 
  •  
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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.

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作为一名学习经管类专业的学生,这本书给了我许多更深入的思考。作者作为人工智能领域的专家,对于因果关系的理解鞭辟入里,使人茅塞顿开。例如开篇提及,在统计学课程上,学生们经常被教导“相关性不代表因果”,但往往很多的教导都止步于此——学生们知道了什么不是因果,却...  

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一 五月,腾讯稳坐公司新闻头条,与今日头条互诉,腾讯视频打造的女团选拔节目《创造101》中的选手王菊逆袭翻盘,现象级的王菊效应在网上发酵。在五月,让我最感兴趣的一条公司新闻也与腾讯有关。5月30日,腾讯官方公众号发布了一篇文章,标题是《她叫Siren,不是人,也可以是...  

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学习一哈

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每个人都是一部因果关系自动机。真要把人脑对因果的思维过程掰扯明白,还真是不容易。作者的因果模型,是把复杂问题简单化的经典例子了。

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让我这个很想粗浅了解统计学的人是一个很好的入门教材。从经典统计学,到贝叶斯,到因果关系都是有很好的介绍。里面的例子很有趣也很烧脑,虽然不知道如何直接利用里面的公式,但是至少知道在统计学之外还有别的工具可以使用。

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Not my book though

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学统计教统计十几年,好多核心的概念第一次看人讲得这么清楚,豁然开朗豁然开朗!

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