Causality pdf epub mobi txt 电子书下载 2024

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Judea Pearl

Cambridge University Press

2009-9-14

484

USD 50.00

Hardcover

9780521895606

图书标签: Causality 统计数学人工智能方法论 Statistics 统计学 Pearl

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发表于2024-12-22

Causality epub 下载 mobi 下载 pdf 下载 txt 电子书下载 2024

Causality pdf epub mobi txt 电子书下载 2024

图书描述

Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal connections and statistical associations. Cited in more than 2,100 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. In this revised edition, Judea Pearl elucidates thorny issues, answers readers' questions, and offers a panoramic view of recent advances in this field of research. Causality will be of interest to students and professionals in a wide variety of fields. Dr Judea Pearl has received the 2011 Rumelhart Prize for his leading research in Artificial Intelligence (AI) and systems from The Cognitive Science Society.

Causality 下载 mobi epub pdf txt 电子书

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Causality pdf epub mobi txt 电子书下载

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用户评价

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对于像economics这样的学科获得explanatory force而言实在是太重要了！Judea Pearl称自己是half-bayesian……

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超级枯燥

评分☆☆☆☆☆

对于像economics这样的学科获得explanatory force而言实在是太重要了！Judea Pearl称自己是half-bayesian……

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“Data is a window to reality and data science is eyeglass that enables us to look through the window. It is not a mirror through which data looks at itself under makeup.”

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A good book worth reading for anyone with math/stat background and also would love to learn causal inference. Core chapters include chap 2,3,4,7. However, the biggest pain point for me reading this book is thinking where and how I could apply the idea and approaches that I learned into my daily data science work. It's still a little bit hard for me