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Markov Chains: Gibbs Fields, Monte Carlo Simulation and Queues Texts in Applied Mathematics, 31 Second Edition 2020
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This thoroughly revised and augmented edition makes complex topics accessible to students and researchers across various fields.
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| Item Weight | 1.5 lbs (680 grams) |
Who Should Buy?
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Graduate Students
Ideal for graduate students focusing on applied mathematics, providing foundational concepts and advanced techniques in Markov chains.
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Researchers
Useful for researchers in statistics or operations research looking to implement Monte Carlo simulations and queueing theory.
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Data Scientists
Data scientists will benefit from its applications in probabilistic modeling and simulation techniques relevant to machine learning.
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Beginner Learners
Not suited for beginners due to advanced mathematical concepts requiring prior knowledge of probability and statistical theory.
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Casual Readers
Those seeking light reading or casual insights into mathematics may find this text too technical and dense.
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Non-Mathematicians
Individuals without a strong mathematical background may struggle with the specialized terminology and complex theories presented.
Product Description
Markov Chains: Gibbs Fields, Monte Carlo Simulation and Queues Texts in Applied Mathematics, 31 Second Edition 2020
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Pros
- Clear and comprehensive explanations
- Great examples and exercises
- Helpful for applied mathematics
- Well-structured content
Cons
- Some sections may be too advanced for beginners.
Product Price History
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Ubuy works hard to protect your security and privacy. Our advanced payment security system ensures confidentiality by encrypting your information during transmission using AES (Advanced Encryption Standards) and SSL (Secure Socket Layer) protocols. Your payment details are 100% secure as we do not share your payment details with third party sellers.
Features & Benefits
- Thoroughly revised and modernized second edition.
- Covers elementary to advanced topics in Markov chains.
- Includes detailed proofs and a self-contained review of probability.
- Features carefully chosen problems at the end of each chapter.
- New material includes advanced algorithms and applications.
- Ideal for students and researchers in multiple disciplines.