LEADER 03669nam 22006615 450 001 9910481960203321 005 20230810171458.0 010 $a3-030-54987-9 024 7 $a10.1007/978-3-030-54987-9 035 $a(CKB)4100000011569003 035 $a(DE-He213)978-3-030-54987-9 035 $a(MiAaPQ)EBC6387615 035 $a(PPN)252508874 035 $a(EXLCZ)994100000011569003 100 $a20201109d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aContinuous-Time Markov Decision Processes $eBorel Space Models and General Control Strategies /$fby Alexey Piunovskiy, Yi Zhang 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XXIV, 583 p. 10 illus., 3 illus. in color.) 225 1 $aProbability Theory and Stochastic Modelling,$x2199-3149 ;$v97 311 $a3-030-54986-0 327 $aForeword -- Preface -- Description of CTMDPs and Preliminaries -- Selected Properties of Controlled Processes -- The Discounted Cost Model -- Reduction to DTMDP: The Total Cost Model -- The Average Cost Model -- The Total Cost Model: General Case -- Gradual-Impulsive Control Models -- Appendices: Miscellaneous Results.-Relevant Definitions and Facts -- Definitions and Facts about Discrete-Time Markov Decision Processes -- Bibliography -- Index -- Notation. . 330 $aThis book offers a systematic and rigorous treatment of continuous-time Markov decision processes, covering both theory and possible applications to queueing systems, epidemiology, finance, and other fields. Unlike most books on the subject, much attention is paid to problems with functional constraints and the realizability of strategies. Three major methods of investigations are presented, based on dynamic programming, linear programming, and reduction to discrete-time problems. Although the main focus is on models with total (discounted or undiscounted) cost criteria, models with average cost criteria and with impulsive controls are also discussed in depth. The book is self-contained. A separate chapter is devoted to Markov pure jump processes and the appendices collect the requisite background on real analysis and applied probability. All the statements in the main text are proved in detail. Researchers and graduate students in applied probability, operational research, statistics and engineering will find this monograph interesting, useful and valuable. . 410 0$aProbability Theory and Stochastic Modelling,$x2199-3149 ;$v97 606 $aProbabilities 606 $aMathematical statistics$xData processing 606 $aSystem theory 606 $aControl theory 606 $aMathematical optimization 606 $aProbability Theory 606 $aStatistics and Computing 606 $aSystems Theory, Control 606 $aOptimization 615 0$aProbabilities. 615 0$aMathematical statistics$xData processing. 615 0$aSystem theory. 615 0$aControl theory. 615 0$aMathematical optimization. 615 14$aProbability Theory. 615 24$aStatistics and Computing. 615 24$aSystems Theory, Control . 615 24$aOptimization. 676 $a519.233 700 $aPiunovskiy$b Alexey$0931561 702 $aZhang$b Yi 702 $aShiryaev$b Albert Nikolaevich 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910481960203321 996 $aContinuous-time Markov decision processes$92095533 997 $aUNINA