Utvidet returrett til 31. januar 2025

Modern Trends in Controlled Stochastic Processes:

Om Modern Trends in Controlled Stochastic Processes:

This book presents state-of-the-art solution methods and applications of stochastic optimal control. It is a collection of extended papers discussed at the traditional Liverpool workshop on controlled stochastic processes with participants from both the east and the west. New problems are formulated, and progresses of ongoing research are reported. Topics covered in this book include theoretical results and numerical methods for Markov and semi-Markov decision processes, optimal stopping of Markov processes, stochastic games, problems with partial information, optimal filtering, robust control, Q-learning, and self-organizing algorithms. Real-life case studies and applications, e.g., queueing systems, forest management, control of water resources, marketing science, and healthcare, are presented. Scientific researchers and postgraduate students interested in stochastic optimal control,- as well as practitioners will find this book appealing and a valuable reference. ¿

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  • Språk:
  • Engelsk
  • ISBN:
  • 9783030769307
  • Bindende:
  • Paperback
  • Sider:
  • 368
  • Utgitt:
  • 6. juni 2022
  • Utgave:
  • 22001
  • Dimensjoner:
  • 155x20x235 mm.
  • Vekt:
  • 557 g.
  • BLACK NOVEMBER
  Gratis frakt
Leveringstid: 2-4 uker
Forventet levering: 19. desember 2024

Beskrivelse av Modern Trends in Controlled Stochastic Processes:

This book presents state-of-the-art solution methods and applications of stochastic optimal control. It is a collection of extended papers discussed at the traditional Liverpool workshop on controlled stochastic processes with participants from both the east and the west. New problems are formulated, and progresses of ongoing research are reported.
Topics covered in this book include theoretical results and numerical methods for Markov and semi-Markov decision processes, optimal stopping of Markov processes, stochastic games, problems with partial information, optimal filtering, robust control, Q-learning, and self-organizing algorithms. Real-life case studies and applications, e.g., queueing systems, forest management, control of water resources, marketing science, and healthcare, are presented.

Scientific researchers and postgraduate students interested in stochastic optimal control,- as well as practitioners will find this book appealing and a valuable reference.
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