MARC Record from harvard_bibliographic_metadata

LEADER: 02506cam a2200337 a 4500
001 012761694-2
005 20110603192250.0
008 101123s2011 ctu b 001 0 eng
010 $a 2010045037
020 $a9780300169690 (hardback)
020 $a0300169698 (hardback)
035 0 $aocn670481486
040 $aDLC$cDLC$dYDX$dBTCTA$dYDXCP$dMOF$dBWX
042 $apcc
050 00 $aQA279.5$b.M415 2011
082 00 $a519.5/42$222
084 $aSCI034000$aMAT015000$2bisacsh
100 1 $aMcGrayne, Sharon Bertsch.
245 14 $aThe theory that would not die :$bhow Bayes' rule cracked the enigma code, hunted down Russian submarines, & emerged triumphant from two centuries of controversy /$cSharon Bertsch McGrayne.
260 $aNew Haven [Conn.] :$bYale University Press,$cc2011.
300 $axiii, 320 p. ;$c25 cm.
520 $a"Bayes' rule appears to be a straightforward, one-line theorem: by updating our initial beliefs with objective new information, we get a new and improved belief. To its adherents, it is an elegant statement about learning from experience. To its opponents, it is subjectivity run amok. In the first-ever account of Bayes' rule for general readers, Sharon Bertsch McGrayne explores this controversial theorem and the human obsessions surrounding it. She traces its discovery by an amateur mathematician in the 1740s through its development into roughly its modern form by French scientist Pierre Simon Laplace. She reveals why respected statisticians rendered it professionally taboo for 150 years--at the same time that practitioners relied on it to solve crises involving great uncertainty and scanty information, even breaking Germany's Enigma code during World War II, and explains how the advent of off-the-shelf computer technology in the 1980s proved to be a game-changer. Today, Bayes' rule is used everywhere from DNA de-coding to Homeland Security. Drawing on primary source material and interviews with statisticians and other scientists, The Theory That Would Not Die is the riveting account of how a seemingly simple theorem ignited one of the greatest controversies of all time"--$cProvided by publisher.
504 $aIncludes bibliographical references (p. 275-306) and index.
650 7 $aMATHEMATICS / History & Philosophy.$2bisacsh
650 7 $aSCIENCE / History.$2bisacsh
650 0 $aBayesian statistical decision theory$xHistory.
899 $a415_565378
899 $a415_565186
988 $a20110504
049 $aCLSL
906 $0DLC