The Book of Why pdf epub mobi txt 电子书下载 2025

☆☆☆☆☆
简体网页||繁体网页

Judea Pearl

Basic Books

2018-5-15

432

USD 32.00

Hardcover

9780465097609

图书标签: 统计逻辑方法论计算机因果哲学科普 AI

喜欢 The Book of Why 的读者还喜欢

下载链接在页面底部

点击这里下载

facebook linkedin mastodon messenger pinterest reddit telegram twitter viber vkontakte whatsapp 复制链接

想要找书就要到小哈图书下载中心

qciss.net

立刻按 ctrl+D收藏本页

你会得到大惊喜!!

发表于2025-01-11

The Book of Why epub 下载 mobi 下载 pdf 下载 txt 电子书下载 2025

The Book of Why pdf epub mobi txt 电子书下载 2025

图书描述

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.

The Book of Why 下载 mobi epub pdf txt 电子书

著者简介

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.

图书目录

The Book of Why pdf epub mobi txt 电子书下载

想要找书就要到小哈图书下载中心

qciss.net

立刻按 ctrl+D收藏本页

你会得到大惊喜!!

用户评价

评分☆☆☆☆☆

详细解读了相关性和因果性的本质区别，提出了基于数学推导，结合symobolic的人类知识和numerical的数据的解决方法

评分☆☆☆☆☆

从公司图书馆借得此书，翻了前两章，结合得到上万维钢的讲解，大致了解了因果关系的重要性和对下一步强AI的启发，为什么要超越相关性去探求因果性。如作者在前言末尾讲到的：“Data do not understand cause and effects; human do. I hope that the new science of casual inference will enable us to better understand how we do it, because there is no better way to understand ourselves than by emulating ourselves. ”

评分☆☆☆☆☆

Heckman, Rubin, Pearl的爱恨情仇啊。From Gelman, Pearl’s obnoxiousness obstructs the disemmination of his ideas. And works by economists are swept under the rug. 画图容易，但用Rubin亦可。同样的问题仍是我们有哪些x该放进来？然后如何从ate到更有意义的参数是根本的识别问题也是modelling problem，这个用图难以。另外经济学家最大的一个贡献（语出Hausman）就是sem；Pearl似乎不能领会我们为何要用sem。端看pearl能不能用dag来写一个市场均衡模型. Imbens最近写了一篇review说经济学家们不用学图论用处不多

评分☆☆☆☆☆

Strong AI和Causal Effect仅依靠当前的统计、机器学习和深度学习方法是不够的，需要建立一套能描述Causal Effect的数学化的语言，在此基础上才能由现在的rung one（描述association）走到rung two（以do-clause描述和推断intervention后产生的结果）和rung three（描述和推断what if have done的结果，即如果做某事后产生的结果，而该事件实际并不一定会发生，而这是人类具备的联想和推断出未知事物因果关系的能力，目前的弱AI并不具备）。深度学习只是一个黑盒，存在可解释性以及仍是一种弱AI的问题。且对因果关系而非相关关系的描述和研究在其他领域也非常需要。