LEADER 01553nam0 2200349 i 450 001 BVE0049898 005 20231121125413.0 010 $a2728302871 100 $a19960820d1994 ||||0itac50 ba 101 | $afre 102 $ait 181 1$6z01$ai $bxxxe 182 1$6z01$an 200 1 $a˜La œBasilicate$echangement social et changement spatial dans une région du Mezzogiorno$fRobert Bergeron 210 $aRoma$c?cole française de Rome$d1994 215 $a713 p., [1! c. di tav.$cill.$d24 cm. 225 | $aCollection de l'École française de Rome$v184 410 0$1001CFI0016288$12001 $aCollection de l'École française de Rome$v184$171202$aÉcole française de Rome$3CFIV017404 606 $aBasilicata$xStoria$2FIR$3RMLC067094$9I 606 $aSocietà$xBasilicata$x1940-1990$2FIR$3RMLC073137$9I 700 1$aBergeron$b, Robert$3BVEV017326$4070$0274594 801 3$aIT$bIT-01$c19960820 850 $aIT-RM028 $aIT-RM0418 $aIT-RM0459 $aIT-FR0017 899 $aBiblioteca Universitaria Alessandrina$bRM028 899 $aBIBLIOTECA ACCADEMIA NAZ. DEI LINCEI E CORSINIANA$bRM0418 899 $aARCHIVIO DI STATO DI ROMA$bRM0459 899 $aBiblioteca umanistica Giorgio Aprea$bFR0017 $eN 912 $aBVE0049898 950 0$aBiblioteca umanistica Giorgio Aprea$d 52MAG 1 Coll L 184$e 52SBA0000221705 VMN RS $fA $h20161123$i20161123 977 $a 01$a 10$a 12$a 52 996 $aBasilicate$9209024 997 $aUNICAS LEADER 03902nam 22006615 450 001 9910755084503321 005 20231024170252.0 010 $a981-9938-38-4 024 7 $a10.1007/978-981-99-3838-4 035 $a(CKB)28550306500041 035 $a(MiAaPQ)EBC30827921 035 $a(Au-PeEL)EBL30827921 035 $a(DE-He213)978-981-99-3838-4 035 $a(PPN)272919268 035 $a(MiAaPQ)EBC30825133 035 $a(Au-PeEL)EBL30825133 035 $a(EXLCZ)9928550306500041 100 $a20231024d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aWAIC and WBIC with R Stan $e100 Exercises for Building Logic /$fby Joe Suzuki 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023. 215 $a1 online resource (241 pages) 311 $a9789819938377 320 $aIncludes bibliographical references and index. 327 $aOver view of Watanabe's Bayes -- Introduction to Watanabe Bayesian Theory -- MCMC and Stan -- Mathematical Preparation -- Regular Statistical Models -- Information Criteria -- Algebraic Geometry -- The Essence of WAOIC -- WBIC and Its Application to Machine Learning. 330 $aMaster the art of machine learning and data science by diving into the essence of mathematical logic with this comprehensive textbook. This book focuses on the widely applicable information criterion (WAIC), also described as the Watanabe-Akaike information criterion, and the widely applicable Bayesian information criterion (WBIC), also described as the Watanabe Bayesian information criterion. This book expertly guides you through relevant mathematical problems while also providing hands-on experience with programming in R and Stan. Whether you?re a data scientist looking to refine your model selection process or a researcher who wants to explore the latest developments in Bayesian statistics, this accessible guide will give you a firm grasp of Watanabe Bayesian Theory. The key features of this indispensable book include: A clear and self-contained writing style, ensuring ease of understanding for readers at various levels of expertise. 100 carefully selected exercises accompanied by solutions in the main text, enabling readers to effectively gauge their progress and comprehension. A comprehensive guide to Sumio Watanabe?s groundbreaking Bayes theory, demystifying a subject once considered too challenging even for seasoned statisticians. Detailed source programs and Stan codes that will enhance readers? grasp of the mathematical concepts presented. A streamlined approach to algebraic geometry topics in Chapter 6, making Bayes theory more accessible and less daunting. Embark on your machine learning and data science journey with this essential textbook and unlock the full potential of WAIC and WBIC today! 606 $aArtificial intelligence 606 $aMachine learning 606 $aComputational intelligence 606 $aArtificial intelligence$xData processing 606 $aArtificial Intelligence 606 $aMachine Learning 606 $aStatistical Learning 606 $aComputational Intelligence 606 $aData Science 615 0$aArtificial intelligence. 615 0$aMachine learning. 615 0$aComputational intelligence. 615 0$aArtificial intelligence$xData processing. 615 14$aArtificial Intelligence. 615 24$aMachine Learning. 615 24$aStatistical Learning. 615 24$aComputational Intelligence. 615 24$aData Science. 676 $a160.5 700 $aSuzuki$b Joe$0846228 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910755084503321 996 $aWAIC and WBIC with R Stan$93590104 997 $aUNINA