Probabilistic Reliability Models helps readers understand and properly use statistical methods

and optimal resource allocation to solve engineering problems.

The author supplies engineers with a deeper understanding of mathematical models while also

equipping mathematically oriented readers with a fundamental knowledge of the engineeringrelated

applications at the center of model building. The book showcases the use of probability

theory and mathematical statistics to solve common, real-world reliability problems. Following

an introduction to the topic, subsequent chapters explore key systems and models including:

• Unrecoverable objects and recoverable systems

• Methods of direct enumeration

• Markov models and heuristic models

• Performance effectiveness

• Time redundancy

• System survivability

• Aging units and their related systems

• Multistate systems

Detailed case studies illustrate the relevance of the discussed methods to real-world technical

projects including software failure avalanches, gas pipelines with underground storage, and

intercontinental ballistic missile (ICBM) control systems. Numerical examples and detailed

explanations accompany each topic, and exercises throughout allow readers to test their

comprehension of the presented material.

Probabilistic Reliability Models is an excellent book for statistics, engineering, and operations

research courses on applied probability at the upper-undergraduate and graduate levels. The

book is also a valuable reference for professionals and researchers working in industry who

would like a mathematical review of reliability models and the relevant applications.