LEADER 01145nam0 2200241 450 001 000045451 005 20181025104140.0 100 $a20181015d19..----km-y0itaa50------ba 200 1 $aGuida pratica Siapa per la lotta antiparassitaria$egli antiparassitari S.I.A.P.A in campagna, in magazzino e nella casa 210 $a[Roma]$c[Società italo americana prodotti antiparassitari]$d[19..] 215 $a4 fogli ripiegati$cill.$d18 cm. 300 $aIn custodia 606 1 $aAntiparassitari 606 2 $aParassiti$xPiante 676 $a632.9$v(22. ed.)$9Danni, infestazioni, malattie delle piante. Soggetti generali relativi al controllo delle infestazioni e delle malattie 801 0$aIT$bUniversità della Basilicata - B.I.A.$gREICAT$2unimarc 912 $a000045451 996 $aGuida pratica Siapa per la lotta antiparassitaria$91535759 997 $aUNIBAS CAT $aSTD119$b01$c20181015$lBAS01$h1655 CAT $aTTM$b30$c20181025$lBAS01$h1041 FMT Z30 -1$lBAS01$LBAS01$mBOOK$1BASA2$APolo Tecnico-Scientifico$2FVIG$BFondo Viggiani$3FVig/43809$643809$5T43809$7Collocato presso la Scuola di Agraria$820181015$f35$FStanza riservata LEADER 00982nam2-2200349---450- 001 990003322030203316 005 20090930144607.0 035 $a000332203 035 $aUSA01000332203 035 $a(ALEPH)000332203USA01 035 $a000332203 100 $a20090930d1980----km-y0itay50------ba 101 $aita 102 $aIT 105 $a||||||||001yy 200 1 $a<> Parte generale$fR. Giuliano 205 $a3 ed. 210 $aRoma$cBulzoni$dcopyr. 1980 215 $a245 p.$d24 cm 225 2 $aQuaderni di chimica degli alimenti$v1 461 1$1001000332202$12001$aQuaderni di chimica degli alimenti$v1 606 0 $aChimica bromatologica-Saggi 676 $a664 700 1$aGIULIANO,$bR.$0479242 801 0$aIT$bsalbc$gISBD 912 $a990003322030203316 951 $a664 GIU (1)$b15714/CBS$c664$d00328667 959 $aBK 969 $aSCI 979 $aRSIAV6$b90$c20090930$lUSA01$h1446 996 $aParte generale$91121259 997 $aUNISA LEADER 05564nam 22007094a 450 001 9911019149503321 005 20200520144314.0 010 $a9786612349652 010 $a9781282349650 010 $a1282349651 010 $a9780470994559 010 $a047099455X 010 $a9780470994542 010 $a0470994541 035 $a(CKB)1000000000687534 035 $a(EBL)470141 035 $a(SSID)ssj0000289709 035 $a(PQKBManifestationID)11255013 035 $a(PQKBTitleCode)TC0000289709 035 $a(PQKBWorkID)10401513 035 $a(PQKB)10347128 035 $a(MiAaPQ)EBC470141 035 $a(OCoLC)232611431 035 $a(Perlego)2751621 035 $a(EXLCZ)991000000000687534 100 $a20071102d2008 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBayesian networks $ea practical guide to applications /$fedited by Olivier Pourret , Patrick Naim, Bruce Marcot 210 $aChichester, West Sussex, Eng. ;$aHoboken, NJ $cJohn Wiley$dc2008 215 $a1 online resource (448 p.) 225 1 $aStatistics in practice 300 $aDescription based upon print version of record. 311 08$a9780470060308 311 08$a0470060301 320 $aIncludes bibliographical references (p. [385]-425) and index. 327 $aBayesian Networks; Contents; Foreword; Preface; 1 Introduction to Bayesian networks; 1.1 Models; 1.2 Probabilistic vs. deterministic models; 1.3 Unconditional and conditional independence; 1.4 Bayesian networks; 2 Medical diagnosis; 2.1 Bayesian networks in medicine; 2.2 Context and history; 2.3 Model construction; 2.4 Inference; 2.5 Model validation; 2.6 Model use; 2.7 Comparison to other approaches; 2.8 Conclusions and perspectives; 3 Clinical decision support; 3.1 Introduction; 3.2 Models and methodology; 3.3 The Busselton network; 3.4 The PROCAM network; 3.5 The PROCAM Busselton network 327 $a3.6 Evaluation3.7 The clinical support tool: TakeHeartII; 3.8 Conclusion; 4 Complex genetic models; 4.1 Introduction; 4.2 Historical perspectives; 4.3 Complex traits; 4.4 Bayesian networks to dissect complex traits; 4.5 Applications; 4.6 Future challenges; 5 Crime risk factors analysis; 5.1 Introduction; 5.2 Analysis of the factors affecting crime risk; 5.3 Expert probabilities elicitation; 5.4 Data preprocessing; 5.5 A Bayesian network model; 5.6 Results; 5.7 Accuracy assessment; 5.8 Conclusions; 6 Spatial dynamics in France; 6.1 Introduction; 6.2 An indicator-based analysis 327 $a6.3 The Bayesian network model6.4 Conclusions; 7 Inference problems in forensic science; 7.1 Introduction; 7.2 Building Bayesian networks for inference; 7.3 Applications of Bayesian networks in forensic science; 7.4 Conclusions; 8 Conservation of marbled murrelets in British Columbia; 8.1 Context/history; 8.2 Model construction; 8.3 Model calibration, validation and use; 8.4 Conclusions/perspectives; 9 Classifiers for modeling of mineral potential; 9.1 Mineral potential mapping; 9.2 Classifiers for mineral potential mapping; 9.3 Bayesian network mapping of base metal deposit; 9.4 Discussion 327 $a9.5 Conclusions10 Student modeling; 10.1 Introduction; 10.2 Probabilistic relational models; 10.3 Probabilistic relational student model; 10.4 Case study; 10.5 Experimental evaluation; 10.6 Conclusions and future directions; 11 Sensor validation; 11.1 Introduction; 11.2 The problem of sensor validation; 11.3 Sensor validation algorithm; 11.4 Gas turbines; 11.5 Models learned and experimentation; 11.6 Discussion and conclusion; 12 An information retrieval system; 12.1 Introduction; 12.2 Overview; 12.3 Bayesian networks and information retrieval; 12.4 Theoretical foundations 327 $a12.5 Building the information retrieval system12.6 Conclusion; 13 Reliability analysis of systems; 13.1 Introduction; 13.2 Dynamic fault trees; 13.3 Dynamic Bayesian networks; 13.4 A case study: The Hypothetical Sprinkler System; 13.5 Conclusions; 14 Terrorism risk management; 14.1 Introduction; 14.2 The Risk Influence Network; 14.3 Software implementation; 14.4 Site Profiler deployment; 14.5 Conclusion; 15 Credit-rating of companies; 15.1 Introduction; 15.2 Naive Bayesian classifiers; 15.3 Example of actual credit-ratings systems; 15.4 Credit-rating data of Japanese companies 327 $a15.5 Numerical experiments 330 $aBayesian Networks, the result of the convergence of artificial intelligence with statistics, are growing in popularity. Their versatility and modelling power is now employed across a variety of fields for the purposes of analysis, simulation, prediction and diagnosis. This book provides a general introduction to Bayesian networks, defining and illustrating the basic concepts with pedagogical examples and twenty real-life case studies drawn from a range of fields including medicine, computing, natural sciences and engineering. Designed to help analysts, engineers, scientists and profe 410 0$aStatistics in practice. 606 $aBayesian statistical decision theory 606 $aMathematical models 615 0$aBayesian statistical decision theory. 615 0$aMathematical models. 676 $a519.5/42 700 $aPourret$b Olivier$01840442 701 $aNaim$b Patrick$0857114 701 $aMarcot$b Bruce$01840443 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911019149503321 996 $aBayesian networks$94419995 997 $aUNINA