Markov analysis is a technique used to study dynamic system behavior. In terms of all reliability analysis techniques, Markov analysis is the only method you can use to accurately model complex systems that include common cause failures, imperfect coverage, shared load redundancy, complex repair policies, degradation, shock effects, induced failures, dependent failures, and other sequence-dependent events. While you can use other modeling methods, such as fault trees and RBDs (reliability block diagrams), to model some of these unique system complexities, Markov analysis provides the broadest capabilities in terms of handling diverse system characteristics.

To complete a Markov analysis, you must construct a Markov diagram, which is also known as a state transition diagram. This diagram is a graphical representation of the system's operational, degraded, and failed states as well as the transitions between these states. Most commonly, transition rates are failure rates or repair rates. The Markov diagram ultimately represents the system as a set of random variables with interdependencies. The results of a Markov analysis can include reliability, availability, MTBF, and failure rate.

If you are unfamiliar with Markov analysis and would like to study this topic in more detail, the following is an excellent resource: