LEADER 05401nam 2201345 450 001 9910463668803321 005 20210515004225.0 010 $a1-4008-5047-9 024 7 $a10.1515/9781400850471 035 $a(CKB)2670000000543850 035 $a(EBL)1584943 035 $a(OCoLC)874965990 035 $a(SSID)ssj0001180246 035 $a(PQKBManifestationID)11786964 035 $a(PQKBTitleCode)TC0001180246 035 $a(PQKBWorkID)11198924 035 $a(PQKB)10909693 035 $a(MiAaPQ)EBC1584943 035 $a(StDuBDS)EDZ0001059570 035 $a(OCoLC)877868292 035 $a(MdBmJHUP)muse43271 035 $a(DE-B1597)453996 035 $a(OCoLC)979686259 035 $a(DE-B1597)9781400850471 035 $a(Au-PeEL)EBL1584943 035 $a(CaPaEBR)ebr10853227 035 $a(CaONFJC)MIL585099 035 $a(EXLCZ)992670000000543850 100 $a20140407h20142014 uy 0 101 0 $aeng 135 $aurun#---|u||u 181 $ctxt 182 $cc 183 $acr 200 10$aTradition and the formation of the Talmud /$fMoulie Vidas 205 $aCore Textbook 210 1$aPrinceton, New Jersey ;$aOxfordshire, England :$cPrinceton University Press,$d2014. 210 4$dİ2014 215 $a1 online resource (251 p.) 300 $aBased on a thesis (Ph. D) Princeton University, 2009. 311 0 $a0-691-17086-X 311 0 $a0-691-15486-4 320 $aIncludes bibliographical references and indexes. 327 $tFront matter --$tContents --$tA Note on Style Conventions --$tIntroduction --$tPart I --$tChapter One. The Alterity of Tradition --$tChapter Two. The Division into Layers --$tChapter Three. Composition as Critique --$tPart II --$tChapter Four. Scholars, Transmitters, and the Making of Talmud --$tChapter Five. The Debate about Recitation --$tChapter Six. Tradition and Vision --$tConclusion --$tAcknowledgments --$tBibliography --$tSource Index --$tSubject Index 330 $aTradition and the Formation of the Talmud offers a new perspective on perhaps the most important religious text of the Jewish tradition. It is widely recognized that the creators of the Talmud innovatively interpreted and changed the older traditions on which they drew. Nevertheless, it has been assumed that the ancient rabbis were committed to maintaining continuity with the past. Moulie Vidas argues on the contrary that structural features of the Talmud were designed to produce a discontinuity with tradition, and that this discontinuity was part and parcel of the rabbis' self-conception. Both this self-conception and these structural features were part of a debate within and beyond the Jewish community about the transmission of tradition. Focusing on the Babylonian Talmud, produced in the rabbinic academies of late ancient Mesopotamia, Vidas analyzes key passages to show how the Talmud's creators contrasted their own voice with that of their predecessors. He also examines Zoroastrian, Christian, and mystical Jewish sources to reconstruct the debates and wide-ranging conversations that shaped the Talmud's literary and intellectual character. 606 $aTalmud$xHistory 606 $aJewish law$xInterpretation and construction 608 $aElectronic books. 610 $aAmoraic tradition. 610 $aBabylonian Talmud. 610 $aBava Qamma. 610 $aChristian literature. 610 $aChristian sources. 610 $aChristianity. 610 $aChristians. 610 $aHekhalot literature. 610 $aHekhalot tradition. 610 $aIsrael. 610 $aJewish culture. 610 $aJewish genealogy. 610 $aJewish history. 610 $aJewish people. 610 $aJewish tradition. 610 $aJews. 610 $aJudaism. 610 $aMesopotamia. 610 $aOral Torah. 610 $aPalestinian Talmud. 610 $aRav Yehuda. 610 $aSar ha-Torah narrative. 610 $aScripture. 610 $aTorah study. 610 $aWritten Torah. 610 $aZoroastrian literature. 610 $aZoroastrian ritual. 610 $aZoroastrian sources. 610 $aanonymous layer. 610 $aapodictic rulings. 610 $aattributed rulings. 610 $aauthority. 610 $acomposition. 610 $adialectic. 610 $adiscontinuity. 610 $agenealogical knowledge. 610 $agenealogical tradition. 610 $aintellectual history. 610 $alayered structure. 610 $aliterary design. 610 $aliturgy. 610 $amystical Jewish sources. 610 $aoral tradition. 610 $arabbinic culture. 610 $arabbis. 610 $arecitation. 610 $areligious text. 610 $asacred texts. 610 $ascholarship. 610 $aself-definition. 610 $aself-presentation. 610 $astam. 610 $asugya. 610 $asugyot. 610 $atanna'im. 610 $atradition. 615 0$aTalmud$xHistory. 615 0$aJewish law$xInterpretation and construction. 676 $a296.1/25066 700 $aVidas$b Moulie$f1983-$01031501 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910463668803321 996 $aTradition and the formation of the Talmud$92448918 997 $aUNINA LEADER 05868nam 2200769 450 001 9910824829403321 005 20230912145345.0 010 $a1-119-14683-6 010 $a1-119-14684-4 010 $a1-119-14682-8 035 $a(CKB)3710000000454783 035 $a(EBL)2009871 035 $a(SSID)ssj0001530643 035 $a(PQKBManifestationID)12562642 035 $a(PQKBTitleCode)TC0001530643 035 $a(PQKBWorkID)11532438 035 $a(PQKB)10822268 035 $a(PQKBManifestationID)16114674 035 $a(PQKB)24273660 035 $a(DLC) 2015019076 035 $a(Au-PeEL)EBL4041085 035 $a(CaPaEBR)ebr11114058 035 $a(CaONFJC)MIL818884 035 $a(OCoLC)908935560 035 $a(CaSebORM)9781119133124 035 $a(MiAaPQ)EBC4041085 035 $a(MiAaPQ)EBC2009871 035 $a(PPN)272710415 035 $a(EXLCZ)993710000000454783 100 $a20151105h20152015 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aFraud analytics using descriptive, predictive, and social network techniques $ea guide to data science for fraud detection /$fBart Baesens, Veronique Van Vlasselaer, Wouter Verbeke 205 $a1st edition 210 1$aHoboken, New Jersey :$cWiley,$d2015. 210 4$dİ2015 215 $a1 online resource (402 p.) 225 1 $aWiley and SAS Business Series 300 $aIncludes index. 311 $a1-119-13312-2 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aCover; Title Page; Copyright; Contents; List of Figures; Foreword; Preface; Acknowledgments; Chapter 1 Fraud: Detection, Prevention, and Analytics!; Introduction; Fraud!; Fraud Detection and Prevention; Big Data for Fraud Detection; Data-Driven Fraud Detection; Fraud-Detection Techniques; Fraud Cycle; The Fraud Analytics Process Model; Fraud Data Scientists; A Fraud Data Scientist Should Have Solid Quantitative Skills; A Fraud Data Scientist Should Be a Good Programmer; A Fraud Data Scientist Should Excel in Communication and Visualization Skills 327 $aA Fraud Data Scientist Should Have a Solid Business Understanding A Fraud Data Scientist Should Be Creative; A Scientific Perspective on Fraud; References; Chapter 2 Data Collection, Sampling, and Preprocessing; Introduction; Types of Data Sources; Merging Data Sources; Sampling; Types of Data Elements; Visual Data Exploration and Exploratory Statistical Analysis; Benford's Law; Descriptive Statistics; Missing Values; Outlier Detection and Treatment; Red Flags; Standardizing Data; Categorization; Weights of Evidence Coding; Variable Selection; Principal Components Analysis; RIDITs 327 $aPRIDIT Analysis Segmentation; References; Chapter 3 Descriptive Analytics for Fraud Detection; Introduction; Graphical Outlier Detection Procedures; Statistical Outlier Detection Procedures; Break-Point Analysis; Peer-Group Analysis; Association Rule Analysis; Clustering; Introduction; Distance Metrics; Hierarchical Clustering; Example of Hierarchical Clustering Procedures; k-Means Clustering; Self-Organizing Maps; Clustering with Constraints; Evaluating and Interpreting Clustering Solutions; One-Class SVMs; References; Chapter 4 Predictive Analytics for Fraud Detection; Introduction 327 $aTarget Definition Linear Regression; Logistic Regression; Basic Concepts; Logistic Regression Properties; Building a Logistic Regression Scorecard; Variable Selection for Linear and Logistic Regression; Decision Trees; Basic Concepts; Splitting Decision; Stopping Decision; Decision Tree Properties; Regression Trees; Using Decision Trees in Fraud Analytics; Neural Networks; Basic Concepts; Weight Learning; Opening the Neural Network Black Box; Support Vector Machines; Linear Programming; The Linear Separable Case; The Linear Nonseparable Case; The Nonlinear SVM Classifier; SVMs for Regression 327 $aOpening the SVM Black Box Ensemble Methods; Bagging; Boosting; Random Forests; Evaluating Ensemble Methods; Multiclass Classification Techniques; Multiclass Logistic Regression; Multiclass Decision Trees; Multiclass Neural Networks; Multiclass Support Vector Machines; Evaluating Predictive Models; Splitting Up the Data Set; Performance Measures for Classification Models; Performance Measures for Regression Models; Other Performance Measures for Predictive Analytical Models; Developing Predictive Models for Skewed Data Sets; Varying the Sample Window; Undersampling and Oversampling 327 $aSynthetic Minority Oversampling Technique (SMOTE) 330 $aDetect fraud earlier to mitigate loss and prevent cascading damage Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques is an authoritative guidebook for setting up a comprehensive fraud detection analytics solution. Early detection is a key factor in mitigating fraud damage, but it involves more specialized techniques than detecting fraud at the more advanced stages. This invaluable guide details both the theory and technical aspects of these techniques, and provides expert insight into streamlining implementation. Coverage includes data gathering, preprocessing, mode 410 0$aWiley and SAS business series. 606 $aFraud$xStatistical methods 606 $aFraud$xPrevention 606 $aCommercial crimes$xPrevention 615 0$aFraud$xStatistical methods. 615 0$aFraud$xPrevention. 615 0$aCommercial crimes$xPrevention. 676 $a364.16/3015195 700 $aBaesens$b Bart$0903326 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910824829403321 996 $aFraud analytics using descriptive, predictive, and social network techniques$93933817 997 $aUNINA