Bayesian recurrent model of reliability growth: parabolic distribution of parameters

This work is devoted to the study of the parabolic distribution of parameters in the Bayesian recurrent model of reliability growth of complex modifiable information systems. In the reliability theory, the reliability of the system depends on the ratio of parameters which are interpreted as indexes of “defectiveness” and “efficiency” of the tool correcting the deficiencies in the system. In the framework of Bayesian models, it is assumed that only the information about the a priori distributions of the system's parameters is given. In this work, the average marginal system reliability is calculated for the a priori parabolic distribution of the parameters. The numerical results for the model examples are obtained.