LEADER 00904nam a2200241 i 4500 001 991000221499707536 005 20020527110859.0 008 010810s1988 it ||| | ita 035 $ab10047268-39ule_inst 035 $aPARLA217655$9ExL 040 $aDip.to Filosofia$bita 082 0 $a299.935 100 1 $aSteiner, Rudolf$033031 245 13$aIl cristianesimo come fatto mistico e i misteri degli antichi /$cRudolf Steiner 260 $aMilano :$bEditrice Antroposofica,$c1988 300 $a136 p. ;$c23 cm. 650 4$aAntroposofia e cristianesimo 907 $a.b10047268$b21-09-06$c27-06-02 912 $a991000221499707536 945 $aLE005 MF 30 A 8$g1$iLE005A-6562$lle005$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i10055332$z27-06-02 996 $aCristianesimo come fatto mistico e i misteri degli antichi$9193246 997 $aUNISALENTO 998 $ale005$b01-01-01$cm$da $e-$fita$git $h3$i1 LEADER 03054nam 2200685Ia 450 001 9910782592403321 005 20230124182730.0 010 $a6611733337 010 $a1-281-73333-4 010 $a9786611733339 010 $a1-60750-298-4 010 $a600-00-0346-3 010 $a1-4337-1131-1 035 $a(CKB)1000000000554071 035 $a(EBL)334196 035 $a(OCoLC)437202842 035 $a(SSID)ssj0000289022 035 $a(PQKBManifestationID)11221425 035 $a(PQKBTitleCode)TC0000289022 035 $a(PQKBWorkID)10386010 035 $a(PQKB)10753325 035 $a(MiAaPQ)EBC334196 035 $a(Au-PeEL)EBL334196 035 $a(CaPaEBR)ebr10216841 035 $a(CaONFJC)MIL173333 035 $a(EXLCZ)991000000000554071 100 $a20071215d2008 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aApproximation methods for efficient learning of Bayesian networks$b[electronic resource] /$fCarsten Riggelsen 210 $aAmsterdam ;$aWashington, DC $cIOS Press$dc2008 215 $a1 online resource (148 p.) 225 1 $aFrontiers in artificial intelligence and applications ;$vv. 168 225 1 $aDissertations in artificial intelligence 300 $aDescription based upon print version of record. 311 $a1-58603-821-4 320 $aIncludes bibliographical references (p. [133]-137). 327 $aTitle page; Contents; Foreword; Introduction; Preliminaries; Learning Bayesian Networks from Data; Monte Carlo Methods and MCMC Simulation; Learning from Incomplete Data; Conclusion; References 330 $aThis publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. The topics discussed are: basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and, the concept of incomplete data. In order t 410 0$aFrontiers in artificial intelligence and applications.$pDissertations in artificial intelligence. 410 0$aFrontiers in artificial intelligence and applications ;$vv. 168. 606 $aBayesian statistical decision theory 606 $aMachine learning 606 $aNeural networks (Computer science) 615 0$aBayesian statistical decision theory. 615 0$aMachine learning. 615 0$aNeural networks (Computer science) 676 $a519.5 676 $a519.5/42 700 $aRiggelsen$b Carsten$01560683 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910782592403321 996 $aApproximation methods for efficient learning of Bayesian networks$93826835 997 $aUNINA