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Random validation and fault detection method in systems implementations

The problematic absence of a structured technique which in its presence ensures both complex infrastructure implementations and software deployments focus on how to utilize prior knowledge of existing infrastructure and on how to apply the information obtained from the preceding and historical outcomes in achieving successful validation cases, has become the central point of discussion in this paper. The concept of Markov process and chain validation is based on the Bayesian approach to parametric models for implementations which can employ prior knowledge, even skills and preceding outcomes for their parameter estimation. This paper proposes an important validation technique drawn from the Markov process and Monte Carlo method and presents statistical analysis to examine the effectiveness of Markov chain with basic random validation.