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47875495
Markov Decision Processes: Discrete Stochastic Dynamic Programming (Wiley Series in Probability and Statistics)
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Položka č.:
47875495
CZK 2757
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Markov Decision Processes: Discrete Stochastic Dynamic Programming represents an up-to-date, unified, and rigorous treatment of theoretical and computational aspects of discrete-time Markov decision processes.
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What Stands Out
Comprehensive Coverage
This book provides an extensive exploration of Markov Decision Processes, making it an essential resource for students and researchers looking to grasp complex concepts in discrete stochastic dynamic programming.
Practical Applications
Includes real-world examples and applications, helping users understand how Markov Decision Processes can be utilized in various fields, enhancing the learning experience for practitioners and theorists alike.
Clear Explanations
Features clear, concise explanations and step-by-step methodologies, making complex mathematical concepts accessible to a wider audience, and ensuring effective learning for both beginners and advanced users.
Detaily produktu
- Part of the Wiley-Interscience Paperback Series
- Brings together various results found in other texts and papers
- Self-contained text with basic mathematical results included
- Includes a wealth of examples, applications, and exercises
- Valuable bibliographical material for gaining perspective on MDP research
- A comprehensive volume on the theoretical and computational aspects of Markov decision processes
| Publisher | Wiley-Interscience |
| Publication date | March 3, 2005 |
| Edition | 1st |
| Language | English |
| Print length | 684 pages |
| ISBN-10 | 0471727822 |
| ISBN-13 | 978-0471727828 |
| Item Weight | 1.8 pounds (820 grams) |
| Dimensions | 6.12 x 1.54 x 9.25 inches (15.5 x 3.9 x 23.5 cm) |
Who Should Buy?
-
Graduate Students
Ideal for students pursuing advanced studies in operations research or artificial intelligence and machine learning applications.
-
Researchers
Beneficial for researchers needing a thorough understanding of Markov processes and their applications in various fields.
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Data Scientists
Useful for data scientists focusing on decision-making processes under uncertainty using stochastic models and algorithms.
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Beginners
Not suitable for novices without prior knowledge of probability theory or dynamic programming concepts.
POPIS PRODUKTU
Markov Decision Processes: Discrete Stochastic Dynamic Programming (Wiley Series in Probability and Statistics)
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Probability & Statistics Editorial Review
Markov Decision Processes: Discrete Stochastic Dynamic Programming (Wiley Series in Probability and Statistics) is highly regarded for its thorough and rigorous coverage of Markov decision processes. Published by Wiley-Interscience, this book spans 684 pages and is particularly recommended for those in the academic field, such as PhD students, who are seeking a foundational understanding of MDP theory. Despite some critiques regarding its lack of updates since its first edition in 2005, readers appreciate the clarity of its presentation which includes important theoretical concepts and problem classes. The book may present challenges in readability due to mathematical notation being small, but it remains an essential resource for anyone serious about the subject.
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Klady
- Thorough coverage of MDP theory and applications
- Highly recommended for doctoral and research students
- Informative resource for foundational learning
- Rigorous yet understandable for masters-level students
- Contains all major problem classes and theoretical insights
Nevýhody
- Mathematical notation can be difficult to read due to small font
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Služby a výhody
- Unique text bringing together many results from other texts and papers.
- Self-contained with basic mathematical results included.
- Provides a rich diet of examples, applications, and exercises.
- Excellent bibliographical material for historical perspective and research potential.
- Great value for advanced-level students, researchers, and professionals.
- Up-to-date, unified, and rigorous treatment of theoretical and computational aspects.
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