LEADER 00815nam0-22002891i-450- 001 990000032580403321 035 $a000003258 035 $aFED01000003258 035 $a(Aleph)000003258FED01 035 $a000003258 100 $a20011111d--------km-y0itay50------ba 101 0 $aita 105 $ay-------001yy 200 1 $a<>edifici per le industrie$fArmando Melis$gcon note tecniche di V. Zignoli. 210 $aTorino$cS. Lattes e C. ed.$d1953 215 $aVIII, 346 p.$cill.$d22 cm 610 0 $aEdifici industriali 676 $a690 700 1$aMelis,$bArmando$02430 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990000032580403321 952 $a13 D 42 10$b20332$fFINBC 959 $aFINBC 996 $aEdifici per le industrie$9105782 997 $aUNINA DB $aING01 LEADER 01033nam a2200253 i 4500 001 991000865409707536 005 20020507174812.0 008 940331s1985 ||| ||| | ita 035 $ab10768178-39ule_inst 035 $aLE01303519$9ExL 040 $aDip.to Matematica$beng 082 0 $a531.11 084 $aAMS 70H35 100 1 $aGiannachi, Antonio$0535307 245 10$aEquazioni di Lagrange e analogie elettromeccaniche. Tesi di laurea /$claureando Antonio Giannachi ; relat. C. Bortone 260 $aLecce :$bUniversità degli studi. Facoltà di Scienze. Corso di laurea in Matematica,$ca.a. 1985-86 650 4$aLagrange equations 700 1 $aBortone, Carlo 907 $a.b10768178$b02-04-14$c28-06-02 912 $a991000865409707536 945 $aLE013 TES 1985/86 GIA1$g1$iLE013N-385$lle013$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i10864763$z28-06-02 996 $aEquazioni di Lagrange e analogie elettromeccaniche. Tesi di laurea$9922382 997 $aUNISALENTO 998 $ale013$b01-01-94$cm$da $e-$feng$gxx $h0$i1 LEADER 04700nam 22006975 450 001 9910734831203321 005 20251216214800.0 010 $a9783031320132 010 $a3031320131 024 7 $a10.1007/978-3-031-32013-2 035 $a(MiAaPQ)EBC30620201 035 $a(Au-PeEL)EBL30620201 035 $a(DE-He213)978-3-031-32013-2 035 $a(PPN)272250279 035 $a(CKB)27483027000041 035 $a(OCoLC)1390561199 035 $a(EXLCZ)9927483027000041 100 $a20230705d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAI-ML for Decision and Risk Analysis $eChallenges and Opportunities for Normative Decision Theory /$fby Louis Anthony Cox Jr 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (443 pages) 225 1 $aInternational Series in Operations Research & Management Science,$x2214-7934 ;$v345 311 08$aPrint version: Cox Jr., Louis Anthony AI-ML for Decision and Risk Analysis Cham : Springer International Publishing AG,c2023 9783031320125 327 $aPart I. Received Wisdom -- 1.Rational Decision and Risk Analysis and Irrational Human Behavior -- 2.Data Analytics and Modeling for Improving Decisions -- 3. Natural, Artificial, and Social Intelligence for Decision-Making -- Part 2: Fundamental Challenges for Practical Decision Theory -- 4.Answerable and Unanswerable Questions in Decision and Risk Analysis -- 5.Decision Theory -- 6.Learning Aversion in Benefit-Cost Analysis with Uncertainty -- Part 3: Ways forward 7.Addressing Wicked Problems and Deep Uncertainties in Risk Analysis -- 8.Muddling Through and Deep Learning for Bureaucratic Decision-Making -- 9.Causally Explainable Decision Recommendations using Causal Artificial Intelligence -- Part 4: Public Health Applications -- 10. Re-Assessing Human Mortality Risks Attributed to Agricultural Air Pollution: Insights from Causal Artificial Intelligence -- 11.Toward more Practical Causal Epidemiology and Health Risk Assessment Using Causal Artificial Intelligence -- 12. Clarifyingthe Meaning of Exposure-Response Curves with Causal AI -- 13. Pushing Back on AI: A Dialogue with ChatGPT -- Index. 330 $aThis book explains and illustrates recent developments and advances in decision-making and risk analysis. It demonstrates how artificial intelligence (AI) and machine learning (ML) have not only benefitted from classical decision analysis concepts such as expected utility maximization but have also contributed to making normative decision theory more useful by forcing it to confront realistic complexities. These include skill acquisition, uncertain and time-consuming implementation of intended actions, open-world uncertainties about what might happen next and what consequences actions can have, and learning to cope effectively with uncertain and changing environments. The result is a more robust and implementable technology for AI/ML-assisted decision-making. The book is intended to inform a wide audience in related applied areas and to provide a fun and stimulating resource for students, researchers, and academics in data science and AI-ML, decision analysis, and other closely linked academic fields. It will also appeal to managers, analysts, decision-makers, and policymakers in financial, health and safety, environmental, business, engineering, and security risk management. 410 0$aInternational Series in Operations Research & Management Science,$x2214-7934 ;$v345 606 $aOperations research 606 $aFinancial risk management 606 $aMachine learning 606 $aArtificial intelligence 606 $aMarkov processes 606 $aOperations Research and Decision Theory 606 $aRisk Management 606 $aMachine Learning 606 $aArtificial Intelligence 606 $aMarkov Process 615 0$aOperations research. 615 0$aFinancial risk management. 615 0$aMachine learning. 615 0$aArtificial intelligence. 615 0$aMarkov processes. 615 14$aOperations Research and Decision Theory. 615 24$aRisk Management. 615 24$aMachine Learning. 615 24$aArtificial Intelligence. 615 24$aMarkov Process. 676 $a658.4030028563 700 $aCox$b Louis A.$cJr.$g(Louis Anthony),$f1957-$01863450 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910734831203321 996 $aAI-ML for Decision and Risk Analysis$94480970 997 $aUNINA