Causation, prediction, and search.
- 作者: Spirtes, Peter.
- 其他作者: Glymour, Clark N. , Scheines, Richard.
- 出版: Cambridge, Mass. : MIT Press ©2000.
- 版本: 2nd ed. /
- 稽核項: 1 online resource (xxi, 543 pages) :illustrations.
- 叢書名: Adaptive computation and machine learning
- 標題: Statistique mathématique. , Probability & StatisticsGeneral. , Electronic books. , MATHEMATICS Probability & Statistics -- General. , COMPUTER SCIENCE/General , MATHEMATICS , Mathematical statistics.
- ISBN: 0262194406 , 9780262194402
- 試查全文@TNUA:
- 附註: Includes bibliographical references (pages 495-529) and index.
- 摘要: The authors address the assumptions and methods that allow us to turn observations into causal knowledge, and use even incomplete causal knowledge in planning and prediction to influence and control our environment.What assumptions and methods allow us to turn observations into causal knowledge, and how can even incomplete causal knowledge be used in planning and prediction to influence and control our environment? In this book Peter Spirtes, Clark Glymour, and Richard Scheines address these questions using the formalism of Bayes networks, with results that have been applied in diverse areas of research in the social, behavioral, and physical sciences.The authors show that although experimental and observational study designs may not always permit the same inferences, they are subject to uniform principles. They axiomatize the connection between causal structure and probabilistic independence, explore several varieties of causal indistinguishability, formulate a theory of manipulation, and develop asymptotically reliable procedures for searching over equivalence classes of causal models, including models of categorical data and structural equation models with and without latent variables.The authors show that the relationship between causality and probability can also help to clarify such diverse topics in statistics as the comparative power of experimentation versus observation, Simpson's paradox, errors in regression models, retrospective versus prospective sampling, and variable selection.The second edition contains a new introduction and an extensive survey of advances and applications that have appeared since the first edition was published in 1993.
- 電子資源: https://dbs.tnua.edu.tw/login?url=https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=138589
- 系統號: 005320010
- 資料類型: 電子書
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- 引用網址: 複製連結
What assumptions and methods allow us to turn observations into causal knowledge, and how can even incomplete causal knowledge be used in planning and prediction to influence and control our environment? In this book Peter Spirtes, Clark Glymour, and Richard Scheines address these questions using the formalism of Bayes networks, with results that have been applied in diverse areas of research in the social, behavioral, and physical sciences. The authors show that although experimental and observational study designs may not always permit the same inferences, they are subject to uniform principles. They axiomatize the connection between causal structure and probabilistic independence, explore several varieties of causal indistinguishability, formulate a theory of manipulation, and develop asymptotically reliable procedures for searching over equivalence classes of causal models, including models of categorical data and structural equation models with and without latent variables. The authors show that the relationship between causality and probability can also help to clarify such diverse topics in statistics as the comparative power of experimentation versus observation, Simpson's paradox, errors in regression models, retrospective versus prospective sampling, and variable selection. The second edition contains a new introduction and an extensive survey of advances and applications that have appeared since the first edition was published in 1993.
來源: Google Book
來源: Google Book
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