001387447 000__ 03557cam\a2200505Ia\4500
001387447 001__ 1387447
001387447 003__ MaCbMITP
001387447 005__ 20240325105114.0
001387447 006__ m\\\\\o\\d\\\\\\\\
001387447 007__ cr\cn\nnnunnun
001387447 008__ 050921s2000\\\\maua\\\\obs\\\001\0\eng\d
001387447 020__ $$a9780262284158$$q(electronic bk.)
001387447 020__ $$a0262284154$$q(electronic bk.)
001387447 020__ $$a0262194406
001387447 020__ $$a9780262194402
001387447 035__ $$a(OCoLC)61677955$$z(OCoLC)61658076$$z(OCoLC)508208521$$z(OCoLC)961894181
001387447 035__ $$a(OCoLC-P)61677955
001387447 040__ $$aOCoLC-P$$beng$$epn$$cOCoLC-P
001387447 050_4 $$aQA276$$b.S65 2000eb
001387447 072_7 $$aMAT$$x029000$$2bisacsh
001387447 08204 $$a519.5$$222
001387447 1001_ $$aSpirtes, Peter.
001387447 24510 $$aCausation, prediction, and search.
001387447 250__ $$a2nd ed. /$$bPeter Spirtes, Clark Glymour, and Richard Scheines ; with additional material by David Heckerman [and others].
001387447 260__ $$aCambridge, Mass. :$$bMIT Press,$$c©2000.
001387447 264_4 $$c©2000
001387447 300__ $$a1 online resource (xxi, 543 pages) :$$billustrations.
001387447 336__ $$atext$$btxt$$2rdacontent
001387447 337__ $$acomputer$$bc$$2rdamedia
001387447 338__ $$aonline resource$$bcr$$2rdacarrier
001387447 4901_ $$aAdaptive computation and machine learning
001387447 506__ $$aAccess limited to authorized users.
001387447 520__ $$aThe 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.
001387447 588__ $$aOCLC-licensed vendor bibliographic record.
001387447 650_0 $$aMathematical statistics.
001387447 653__ $$aCOMPUTER SCIENCE/General
001387447 655_0 $$aElectronic books
001387447 7001_ $$aGlymour, Clark N.
001387447 7001_ $$aScheines, Richard.
001387447 852__ $$bebk
001387447 85640 $$3MIT Press$$uhttps://univsouthin.idm.oclc.org/login?url=https://doi.org/10.7551/mitpress/1754.001.0001?locatt=mode:legacy$$zOnline Access through The MIT Press Direct
001387447 85642 $$3OCLC metadata license agreement$$uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
001387447 909CO $$ooai:library.usi.edu:1387447$$pGLOBAL_SET
001387447 980__ $$aBIB
001387447 980__ $$aEBOOK
001387447 982__ $$aEbook
001387447 983__ $$aOnline