| Author | Ching W.-K., Ng M.K. |
| Publisher | Springer |
| Year | 2006 |
| Language | English |
| Pages | 205 |
| Size | 1.41 MB |
| Extension |
Summary
"Markov Chains: Models, Algorithms and Applications" provides a comprehensive introduction to Markov chains, covering both theoretical foundations and practical applications. The book is structured into eight chapters, each focusing on different aspects of Markov chains:
- Introduction to Markov Chains: Covers the classical theory of both discrete and continuous time Markov chains, including the relationship between Markov chains and matrix theory. Introduces classical iterative methods for solving linear systems to find the stationary distribution of a Markov chain.
- Applications in Queueing Systems and Web Ranking: Discusses the use of continuous time Markov chains to model queueing systems and discrete time Markov chains for computing PageRank, the ranking algorithm used by search engines.
- Markovian Models for Manufacturing Systems: Presents models for manufacturing and re-manufacturing systems, providing closed-form solutions and fast numerical algorithms for solving these systems.
- Hidden Markov Models (HMMs): Introduces HMMs and provides fast numerical algorithms for estimating model parameters. Includes an application of HMMs for customer classification.
- Markov Decision Processes (MDPs): Discusses the use of MDPs for calculating Customer Lifetime Values (CLV), an important concept in marketing management.
- Higher-Order Markov Chain Models: Introduces parsimonious higher-order Markov chain models and efficient estimation methods based on linear programming. Applications to demand predictions, inventory control, and financial risk measurement are also presented.
- Multivariate Markov Models: Introduces a class of parsimonious multivariate Markov models and efficient estimation methods. Applications to demand predictions, inventory control policy, and modeling credit ratings data are discussed.
- Advanced Hidden Markov Models: Revisits HMMs and introduces a new class of HMMs with efficient algorithms for estimating model parameters.
The book is an essential resource for researchers and practitioners in the field, offering detailed algorithms, practical examples, and case studies to illustrate the applications of Markov chains in various domains.
Key Features
- Comprehensive coverage of Markov chain models
- Detailed algorithms and their applications
- Practical examples and case studies
About Author
Ching W.-K. and Ng M.K. are renowned experts in the field of Markov chains and their applications. They have authored numerous research papers and books on the subject, contributing significantly to the advancement of the field.
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Frequently Asked Questions
Q: What can I learn by reading this book?
A: You can learn about Markov chain models, algorithms, and their applications in various fields.
Q: Is this book suitable for beginners?
A: Yes, it provides a comprehensive introduction to the topic, making it suitable for beginners.
Q: Is this book recommended for professionals?
A: Absolutely, it offers in-depth coverage and practical examples that are valuable for professionals.
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