Markov Processes for Stochastic Modeling

• Markov Processes for Stochastic Modeling

• Explores dynamic programming in controlled models
• Discusses numerical methods for Markov chains
• Details applications in queueing and inventory systems
• Covers advanced topics such as hidden Markov models
• Presents stochastic ordering and bounds
• Includes numerous examples and exercises

Markov Processes for Stochastic Modeling offers a comprehensive introduction to Markov processes and their applications. It explores the theoretical foundations, covering recurrence, transience, stationary distributions, and limit theorems. Advanced areas like continuous-time Markov chains, Poisson processes, birth-and-death processes, and queueing theory are discussed. The book emphasizes practical applications in finance, biology, engineering, and computer science, making it ideal for students, researchers, and practitioners seeking a solid understanding of Markov processes for solving real-world problems.