The mind's arrows : Bayes nets and graphical causal models in psychology /

In 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...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Glymour, Clark N.
Μορφή: Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Cambridge, Mass. : MIT Press, �2001.
Σειρά:Bradford Bks.
Θέματα:
Διαθέσιμο Online:http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=78169
LEADER 04903cam a2200745Ia 4500
001 ocm53193021
003 OCoLC
005 20190114101359.0
006 m o d
007 cr cn|||||||||
008 031014s2001 maua ob 001 0 eng d
040 |a N$T  |b eng  |e pn  |c N$T  |d OCLCQ  |d YDXCP  |d OCLCQ  |d N$T  |d OCLCQ  |d OCLCF  |d NLGGC  |d OCLCO  |d OCLCQ  |d ZXC  |d VVL  |d MYG  |d ZCU  |d COO  |d OCLCQ  |d MYG  |d TOA  |d LIP  |d OCLCQ  |d RRP  |d OCLCA  |d GRG  |d CEF  |d VNS  |d VTS 
066 |c Zsym 
019 |a 50324774  |a 60654597  |a 508274176  |a 961602364  |a 962684992  |a 990759254 
020 |a 9780262273961  |q (electronic bk.) 
020 |a 0262273969  |q (electronic bk.) 
020 |a 9780262318730  |q (electronic bk.) 
020 |a 0262318733  |q (electronic bk.) 
020 |a 9780262072205 
020 |a 0262072203  |q (Trade Cloth) 
029 1 |a AU@  |b 000051395458 
029 1 |a DEBSZ  |b 412843471 
029 1 |a GBVCP  |b 801076684 
035 |a (OCoLC)53193021  |z (OCoLC)50324774  |z (OCoLC)60654597  |z (OCoLC)508274176  |z (OCoLC)961602364  |z (OCoLC)962684992  |z (OCoLC)990759254 
037 |b 00015994 
050 4 |a BF38.5  |b .G59 2001eb 
060 4 |a ELECTRONIC BOOK 
072 7 |a PSY  |x 000000  |2 bisacsh 
082 0 4 |a 150/.1  |2 22 
049 |a MAIN 
100 1 |a Glymour, Clark N. 
245 1 4 |a The mind's arrows :  |b Bayes nets and graphical causal models in psychology /  |c Clark Glymour. 
260 |a Cambridge, Mass. :  |b MIT Press,  |c �2001. 
300 |a 1 online resource (xv, 222 pages) :  |b illustrations. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a Bradford Bks. 
500 |a "A Bradford book." 
504 |a Includes bibliographical references (pages 205-217) and index. 
505 0 0 |g 1.  |t Introduction --  |g 2.  |t Android epistemology for babies --  |g 3.  |t Another way for nerds to make babies : the frame problem and causal inference in developmental psychology --  |g 4.  |t A puzzling experiment --  |g 5.  |t The puzzle resolved --  |g 6.  |t Marilyn vos Savant meets Rescorla and Wagner --  |g 7.  |t Cheng models --  |g 8.  |t Learning procedures --  |g 9.  |t Representation and rationality : the case of backward blocking --  |g 10.  |t Cognitive parts : from Freud to Farah --  |g 11.  |t Inference to cognitive architecture from individual case studies --  |g 12.  |t Group data in cognitive neuropsychology --  |g 13.  |t The explanatory power of lesioning neural nets --  |g 14.  |t Social statistics and genuine inquiry : the case of The bell curve. 
588 0 |a Print version record. 
520 8 |a In 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. 
546 |a English. 
590 |a OCLC  |b WorldCat Holdings 
590 |a eBooks on EBSCOhost  |b All EBSCO eBooks 
650 0 |a Psychology  |x Methodology. 
650 0 |a Prediction theory. 
650 0 |a Causation. 
650 7 |a PSYCHOLOGY  |x General.  |2 bisacsh 
650 7 |a Causation.  |2 fast  |0 (OCoLC)fst00849829 
650 7 |a Prediction theory.  |2 fast  |0 (OCoLC)fst01075037 
650 7 |a Psychology  |x Methodology.  |2 fast  |0 (OCoLC)fst01081483 
650 2 |a Psychology  |x methods. 
650 2 |a Causality. 
655 4 |a Electronic books. 
776 0 8 |i Print version:  |a Glymour, Clark N.  |t Mind's arrows.  |d Cambridge, Mass. : MIT Press, �2001  |z 0262072203  |w (DLC) 2001032623  |w (OCoLC)46908702 
830 0 |a Bradford Bks. 
856 4 0 |u http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=78169 
880 4 |6 264-00  |c �2001 
938 |a EBSCOhost  |b EBSC  |n 78169 
938 |a YBP Library Services  |b YANK  |n 2335498 
938 |a YBP Library Services  |b YANK  |n 3411037 
994 |a 92  |b GRPAT 
999 |c 138937  |d 138937