Modern Trends in Controlled Stochastic Processes:

Yi Zhang

ISBN 10: 3030769275 / ISBN 13: 9783030769277

Verlag: Springer International Publishing Jun 2021, 2021

Beschreibung

This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents state-of-the-art solution methods and applications of stochastic optimal control. It is acollection 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 optimalcontrol,-as well as practitioners will find this book appealing and a valuable reference. 368 pp. Englisch.

Bestandsnummer des Verkäufers 9783030769277

Bibliografische Details

• Titel: Modern Trends in Controlled Stochastic Processes:
• Verlag: Springer International Publishing Jun 2021
• Erscheinungsjahr: 2021
• Sprache: Englisch
• ISBN-10: 3030769275
• ISBN-13: 9783030769277
• Einband: Buch
• Zustand: Neu
• Abmessungen: 241B x 160 H x 26 T Millimeter Breite: 241 mm, Höhe: 160 mm, Tiefe: 26 mm
• Gewicht: 717 Gramm

Über diesen Titel

Inhaltsangabe

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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