Introduction to Probability Models: Thirteenth Edition is available in two manageable volumes: an Elementary edition appropriate for undergraduate use and an Advanced edition for graduate use. Together, and through their hallmark exercises and real examples, both versions offer a comprehensive foundation of this key subject with applications across engineering, computer science, management science, the physical and social sciences and operations research. Users will find comprehensive information that introduces them to the foundations of probability modeling and stochastic processes from Random Variables, to Markov Chains and Renewal Theory.

• Provides an update to the prior edition, which was a 2020 Textbook Excellence Award (College) (Texty) Winner from the Textbook and Academic Authors Association
• Focuses on market feedback and usage to provide appropriate undergraduate coverage
• Retains the valuable organization and trusted undergraduate coverage that students and professors have relied on since 1972

1. Introduction to Probability Theory
2. Random Variables
3. Conditional Probability and Conditional Expectation
4. Markov Chains
5. The Exponential Distribution and the Poisson Process
6. Continuous-Time Markov Chains
7. Renewal Theory and Its Applications

A trusted market leader for four decades, Sheldon Ross’s Introduction to Probability Models is now available in two more manageable volumes: an Elementary edition appropriate for undergraduate use, and an Advanced edition for graduate use. Together, and through their hallmark exercises and real examples, both versions offer a comprehensive foundation of this key subject with applications across engineering, computer science, management science, the physical and social sciences and operations research.

Introduction to Probability Models introduces the reader to the foundations of probability modelling and stochastic processes from Random Variables, to Markov Chains, to Renewal Theory.