LEADER 02569nam0 22005893i 450 001 VAN0250212 005 20230605085410.962 017 70$2N$a9789811329715 100 $a20220913d2020 |0itac50 ba 101 $aeng 102 $aSG 105 $a|||| ||||| 200 1 $aMonte Carlo Methods$fAdrian Barbu, Song-Chun Zhu 210 $aSingapore$cSpringer$d2020 215 $axvi, 422 p.$cill.$d24 cm 500 1$3VAN0250213$aMonte Carlo Methods$92909542 606 $a68-XX$xComputer science [MSC 2020]$3VANC019670$2MF 606 $a65-XX$xNumerical analysis [MSC 2020]$3VANC019772$2MF 606 $a65C05$xMonte Carlo methods [MSC 2020]$3VANC020429$2MF 606 $a62H12$xEstimation in multivariate analysis [MSC 2020]$3VANC021210$2MF 606 $a62-XX$xStatistics [MSC 2020]$3VANC022998$2MF 606 $a62J12$xGeneralized linear models (logistic models) [MSC 2020]$3VANC025019$2MF 606 $a62J10$xAnalysis of variance and covariance (ANOVA) [MSC 2020]$3VANC026516$2MF 606 $a62G05$xNonparametric estimation [MSC 2020]$3VANC029306$2MF 606 $a62G08$xNonparametric regression and quantile regression [MSC 2020]$3VANC029587$2MF 610 $aArtificial Intelligence$9KW:K 610 $aComputer Graphics$9KW:K 610 $aComputer vision$9KW:K 610 $aData Driven Markov Chain Monte Carlo$9KW:K 610 $aEnergy Landscape Mapping$9KW:K 610 $aGibbs Sampler$9KW:K 610 $aHamiltonian Monte Carlo$9KW:K 610 $aLangevin Monte Carlo$9KW:K 610 $aMachine learning$9KW:K 610 $aMarkov Chain Monte Carlo$9KW:K 610 $aMetropolis-Hastings$9KW:K 610 $aMonte Carlo Methods$9KW:K 610 $aSequential Monte Carlo$9KW:K 610 $aStochastic Gradient Descent$9KW:K 610 $aSwendsen-Wang Cuts$9KW:K 620 $aSG$dSingapore$3VANL000061 700 1$aBarbu$bAdrian$3VANV204480$01002774 701 1$aZhu$bSong-Chun$3VANV204481$01254935 712 $aSpringer $3VANV108073$4650 801 $aIT$bSOL$c20240614$gRICA 856 4 $uhttp://doi.org/10.1007/978-981-13-2971-5$zE-book ? Accesso al full-text attraverso riconoscimento IP di Ateneo, proxy e/o Shibboleth 899 $aBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA$1IT-CE0120$2VAN08 912 $fN 912 $aVAN0250212 950 $aBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA$d08CONS e-book 4978 $e08eMF4978 20220913 996 $aMonte Carlo Methods$92909542 997 $aUNICAMPANIA