Dynamic Optimization Deterministic and Stochastic Models /

This book explores discrete-time dynamic optimization and provides a detailed introduction to both deterministic and stochastic models. Covering problems with finite and infinite horizon, as well as Markov renewal programs, Bayesian control models and partially observable processes, the book focuses...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Hinderer, Karl (Συγγραφέας), Rieder, Ulrich (Συγγραφέας), Stieglitz, Michael (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2016.
Σειρά:Universitext,
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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100 1 |a Hinderer, Karl.  |e author. 
245 1 0 |a Dynamic Optimization  |h [electronic resource] :  |b Deterministic and Stochastic Models /  |c by Karl Hinderer, Ulrich Rieder, Michael Stieglitz. 
264 1 |a Cham :  |b Springer International Publishing :  |b Imprint: Springer,  |c 2016. 
300 |a XXII, 530 p. 22 illus.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
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490 1 |a Universitext,  |x 0172-5939 
505 0 |a Introduction and Organization of the Book -- Part I Deterministic Models -- Part II Markovian Decision Processes -- Part III Generalizations of Markovian Decision Processes -- Part IV Appendix. 
520 |a This book explores discrete-time dynamic optimization and provides a detailed introduction to both deterministic and stochastic models. Covering problems with finite and infinite horizon, as well as Markov renewal programs, Bayesian control models and partially observable processes, the book focuses on the precise modelling of applications in a variety of areas, including operations research, computer science, mathematics, statistics, engineering, economics and finance. Dynamic Optimization is a carefully presented textbook which starts with discrete-time deterministic dynamic optimization problems, providing readers with the tools for sequential decision-making, before proceeding to the more complicated stochastic models. The authors present complete and simple proofs and illustrate the main results with numerous examples and exercises (without solutions). With relevant material covered in four appendices, this book is completely self-contained. 
650 0 |a Mathematics. 
650 0 |a System theory. 
650 0 |a Operations research. 
650 0 |a Management science. 
650 0 |a Mathematical optimization. 
650 0 |a Probabilities. 
650 1 4 |a Mathematics. 
650 2 4 |a Operations Research, Management Science. 
650 2 4 |a Systems Theory, Control. 
650 2 4 |a Discrete Optimization. 
650 2 4 |a Probability Theory and Stochastic Processes. 
700 1 |a Rieder, Ulrich.  |e author. 
700 1 |a Stieglitz, Michael.  |e author. 
710 2 |a SpringerLink (Online service) 
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776 0 8 |i Printed edition:  |z 9783319488134 
830 0 |a Universitext,  |x 0172-5939 
856 4 0 |u http://dx.doi.org/10.1007/978-3-319-48814-1  |z Full Text via HEAL-Link 
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950 |a Mathematics and Statistics (Springer-11649)