LEADER 02366nam0 2200493 i 450 001 VAN0114646 005 20220303015228.210 017 70$2N$a978-3-319-48814-1 100 $a20180208d2016 |0itac50 ba 101 $aeng 102 $aCH 105 $a|||| ||||| 200 1 $aDynamic optimization$edeterministic and stochastic models$fKarl Hinderer, Ulrich Rieder, Michael Stieglitz 210 $a[Cham]$cSpringer$d2016 215 $aXXII, 530 p.$cill.$d24 cm 410 1$1001VAN0024506$12001 $aUniversitext$1210 $aBerlin [etc]$cSpringer$d1930- 500 1$3VAN0242111$aDynamic optimization$91523279 606 $a93E20$xOptimal stochastic control [MSC 2020]$3VANC019946$2MF 606 $a90B10$xDeterministic network models in operations research [MSC 2020]$3VANC021354$2MF 606 $a90-XX$xOperations research, mathematical programming [MSC 2020]$3VANC025650$2MF 606 $a60J20$xApplications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) [MSC 2020]$3VANC029367$2MF 606 $a90C40$xMarkov and semi-Markov decision processes [MSC 2020]$3VANC033686$2MF 606 $a90C39$xDynamic programming [MSC 2020]$3VANC033782$2MF 610 $aBayesian control models$9KW:K 610 $aDiscrete-time multi-stage optimization$9KW:K 610 $aDynamic Programming$9KW:K 610 $aMarkov decision processes$9KW:K 610 $aMarkov renewal programs$9KW:K 610 $aNetworks$9KW:K 610 $aPartially observable processes$9KW:K 610 $aStochastic optimal control$9KW:K 620 $aCH$dCham$3VANL001889 700 1$aHinderer$bKarl$3VANV088702$0755886 701 1$aRieder$bUlrich$3VANV088703$0512174 701 1$aStieglitz$bMichael$3VANV088704$0755887 712 $aSpringer $3VANV108073$4650 801 $aIT$bSOL$c20240614$gRICA 856 4 $uhttp://dx.doi.org/10.1007/978-3-319-48814-1$zE-book ? Accesso al full-text attraverso riconoscimento IP di Ateneo, proxy e/o Shibboleth 899 $aBIBLIOTECA CENTRO DI SERVIZIO SBA$2VAN15 912 $fN 912 $aVAN0114646 950 $aBIBLIOTECA CENTRO DI SERVIZIO SBA$d15CONS SBA EBOOK 2208 $e15EB 2208 20180208 996 $aDynamic optimization$91523279 997 $aUNICAMPANIA