Derived from a course taught by the authors for nearly forty years, Mathematical Models for Systems Reliability covers fundamental models, as well as statistical inference techniques that can be applied to reliability data. The authors provide readers with a background in the Poisson Process and associated probability laws and statistical life length distributions. The book then moves on to cover reliability analysis, including both one and two-unit systems, and advanced topics, such as continuous-time and embedded Markov chains. It illustrates the models and methods discussed with numerous examples. It also features a large number of problems to simulate further understanding.

The book is ideally suited to a one semester course on reliability theory, taught out of mathematics/statistics and/or engineering departments, with students requiring just a basic knowledge of calculus, probability and linear algebra.