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47875495
Markov Decision Processes: Discrete Stochastic Dynamic Programming (Wiley Series in Probability and Statistics)
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Item #:
47875495
XAF 71678
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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.
Product Details
- 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.
-
Data Scientists
Useful for data scientists focusing on decision-making processes under uncertainty using stochastic models and algorithms.
-
Beginners
Not suitable for novices without prior knowledge of probability theory or dynamic programming concepts.
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Features & Benefits
- 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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