TY  - BOOK
AU  - Guo,Xianping
AU  - Hernández-Lerma,Onésimo
ED  - SpringerLink (Online service)
TI  - Continuous-Time Markov Decision Processes: Theory and Applications 
T2  - Stochastic Modelling and Applied Probability,
SN  - 9783642025471
AV  - QA402.5-402.6 
U1  - 519.6 23
PY  - 2009///
CY  - Berlin, Heidelberg
PB  - Springer Berlin Heidelberg
KW  - Mathematical optimization
KW  - Distribution (Probability theory
KW  - Optimization
KW  - Probability Theory and Stochastic Processes
KW  - Operations Research, Management Science
N1  - and Summary -- Continuous-Time Markov Decision Processes -- Average Optimality for Finite Models -- Discount Optimality for Nonnegative Costs -- Average Optimality for Nonnegative Costs -- Discount Optimality for Unbounded Rewards -- Average Optimality for Unbounded Rewards -- Average Optimality for Pathwise Rewards -- Advanced Optimality Criteria -- Variance Minimization -- Constrained Optimality for Discount Criteria -- Constrained Optimality for Average Criteria
N2  - Continuous-time Markov decision processes (MDPs), also known as controlled Markov chains, are used for modeling decision-making problems that arise in operations research (for instance, inventory, manufacturing, and queueing systems), computer science, communications engineering, control of populations (such as fisheries and epidemics), and management science, among many other fields. This volume provides a unified, systematic, self-contained presentation of recent developments on the theory and applications of continuous-time MDPs. The MDPs in this volume include most of the cases that arise in applications, because they allow unbounded transition and reward/cost rates. Much of the material appears for the first time in book form
UR  - https://doi.org/10.1007/978-3-642-02547-1
ER  -