LEADER 02414nam 22005774a 450 001 9910455273703321 005 20200520144314.0 010 $a1-282-36766-8 010 $a9786612367663 010 $a0-19-974254-5 035 $a(CKB)1000000000799593 035 $a(SSID)ssj0000305855 035 $a(PQKBManifestationID)11226326 035 $a(PQKBTitleCode)TC0000305855 035 $a(PQKBWorkID)10293612 035 $a(PQKB)10288269 035 $a(SSID)ssj0001148043 035 $a(PQKBManifestationID)12549502 035 $a(PQKBTitleCode)TC0001148043 035 $a(PQKBWorkID)11143337 035 $a(PQKB)10736210 035 $a(MiAaPQ)EBC3053529 035 $a(Au-PeEL)EBL3053529 035 $a(CaPaEBR)ebr10346503 035 $a(CaONFJC)MIL236766 035 $a(OCoLC)922969974 035 $a(EXLCZ)991000000000799593 100 $a20051215d2006 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aPeter, Paul, and Mary Magdalene$b[electronic resource] $ethe followers of Jesus in history and legend /$fBart D. Ehrman 210 $aOxford ;$aNew York $cOxford University Press$d2006 215 $axv, 285 p. $cill 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a0-19-534350-6 311 $a0-19-530013-0 320 $aIncludes bibliographical references (p. 261-272) and index. 330 $aHistorian of religion Ehrman takes readers on a tour of the early Christian church, illuminating the lives of three of Jesus' most intriguing followers: Simon Peter, Paul of Tarsus, and Mary Magdalene. What do the writings of the New Testament tell us about each of these key followers of Christ? What legends have sprung up about them in the centuries after their deaths? Was Paul bow-legged and bald? Was Peter crucified upside down? Was Mary Magdalene a prostitute? Ehrman separates fact from fiction, presenting complicated historical issues in a clear and informative way and relating anecdotes culled from the traditions of these three followers.--From publisher description. 608 $aElectronic books. 676 $a225.9/22 700 $aEhrman$b Bart D$0476550 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910455273703321 996 $aPeter, Paul, and Mary Magdalene$92170559 997 $aUNINA LEADER 03243nam 2200493 450 001 9910672446803321 005 20230523120646.0 010 $a3-031-22438-8 024 7 $a10.1007/978-3-031-22438-6 035 $a(MiAaPQ)EBC7203073 035 $a(Au-PeEL)EBL7203073 035 $a(CKB)26154972700041 035 $a(DE-He213)978-3-031-22438-6 035 $a(PPN)268209863 035 $a(EXLCZ)9926154972700041 100 $a20230523d2023 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBayesian tensor decomposition for signal processing and machine learning $emodeling, tuning-free algorithms and applications /$fLei Cheng, Zhongtao Chen, and Yik-Chung Wu 205 $a1st ed. 2023. 210 1$aCham, Switzerland :$cSpringer,$d[2023] 210 4$dİ2023 215 $a1 online resource (189 pages) 311 08$aPrint version: Cheng, Lei Bayesian Tensor Decomposition for Signal Processing and Machine Learning Cham : Springer International Publishing AG,c2023 9783031224379 327 $aTensor decomposition: Basics, algorithms, and recent advances -- Bayesian learning for sparsity-aware modeling -- Bayesian tensor CPD: Modeling and inference -- Bayesian tensor CPD: Performance and real-world applications -- When stochastic optimization meets VI: Scaling Bayesian CPD to massive data -- Bayesian tensor CPD with nonnegative factors -- Complex-valued CPD, orthogonality constraint and beyond Gaussian noises -- Handling missing value: A case study in direction-of-arrival estimation -- From CPD to other tensor decompositions. 330 $aThis book presents recent advances of Bayesian inference in structured tensor decompositions. It explains how Bayesian modeling and inference lead to tuning-free tensor decomposition algorithms, which achieve state-of-the-art performances in many applications, including blind source separation; social network mining; image and video processing; array signal processing; and, wireless communications. The book begins with an introduction to the general topics of tensors and Bayesian theories. It then discusses probabilistic models of various structured tensor decompositions and their inference algorithms, with applications tailored for each tensor decomposition presented in the corresponding chapters. The book concludes by looking to the future, and areas where this research can be further developed. Bayesian Tensor Decomposition for Signal Processing and Machine Learning is suitable for postgraduates and researchers with interests in tensor data analytics and Bayesian methods. 606 $aMachine learning$xStatistical methods 606 $aSignal processing$xStatistical methods 615 0$aMachine learning$xStatistical methods. 615 0$aSignal processing$xStatistical methods. 676 $a006.31 700 $aCheng$b Lei$01335386 702 $aChen$b Zhongtao 702 $aWu$b Yik-Chung 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910672446803321 996 $aBayesian Tensor Decomposition for Signal Processing and Machine Learning$93049326 997 $aUNINA