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001387396 1001_ $$aGlymour, Clark N.
001387396 24514 $$aThe mind's arrows :$$bBayes nets and graphical causal models in psychology /$$cClark Glymour.
001387396 260__ $$aCambridge, Mass. :$$bMIT Press,$$c©2001.
001387396 264_4 $$c©2001
001387396 300__ $$a1 online resource (xv, 222 pages) :$$billustrations.
001387396 336__ $$atext$$btxt$$2rdacontent
001387396 337__ $$acomputer$$bc$$2rdamedia
001387396 338__ $$aonline resource$$bcr$$2rdacarrier
001387396 4901_ $$aBradford Bks.
001387396 500__ $$a"A Bradford book."
001387396 506__ $$aAccess limited to authorized users.
001387396 5208_ $$aIn recent years, small groups of statisticians, computer scientists, and philosophers have developed an account of how partial causal knowledge can be used to compute the effect of actions and how causal relations can be learned, at least by computers. The representations used in the emerging theory are causal Bayes nets or graphical causal models. In his new book, Clark Glymour provides an informal introduction to the basic assumptions, algorithms, and techniques of causal Bayes nets and graphical causal models in the context of psychological examples. He demonstrates their potential as a powerful tool for guiding experimental inquiry and for interpreting results in developmental psychology, cognitive neuropsychology, psychometrics, social psychology, and studies of adult judgment. Using Bayes net techniques, Glymour suggests novel experiments to distinguish among theories of human causal learning and reanalyzes various experimental results that have been interpreted or misinterpreted - without the benefit of Bayes nets and graphical causal models. The capstone illustration is an analysis of the methods used in Herrnstein and Murray's book The Bell Curve ; Glymour argues that new, more reliable methods of data analysis, based on Bayes nets representations, would lead to very different conclusions from those advocated by Herrnstein and Murray.
001387396 588__ $$aOCLC-licensed vendor bibliographic record.
001387396 650_0 $$aPsychology$$xMethodology.
001387396 650_0 $$aPrediction theory.
001387396 650_0 $$aCausation.
001387396 653__ $$aCOGNITIVE SCIENCES/General
001387396 653__ $$aCOGNITIVE SCIENCES/Psychology/Cognitive Psychology
001387396 655_0 $$aElectronic books
001387396 852__ $$bebk
001387396 85640 $$3MIT Press$$uhttps://univsouthin.idm.oclc.org/login?url=https://doi.org/10.7551/mitpress/4638.001.0001?locatt=mode:legacy$$zOnline Access through The MIT Press Direct
001387396 85642 $$3OCLC metadata license agreement$$uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
001387396 909CO $$ooai:library.usi.edu:1387396$$pGLOBAL_SET
001387396 980__ $$aBIB
001387396 980__ $$aEBOOK
001387396 982__ $$aEbook
001387396 983__ $$aOnline