Estimating Weights of Evaluation Factors on Basis of Monte Carlo Simulation

Although the value of factor weight in an evaluation work is deterministic, the solving process is random, so connection between weight solution with digital characteristics or distribution functions of specific random variables or random process could be build. Using stochastic simulation method to get a lot of random solutions to the problem, expectation of the random solutions can be used as a estimation solution. On basis of idea of Monte Carlo simulation, this paper analyzed the probability process of calculating factor weight, and provided the procedures of estimating factor weight by means of Monte Carlo simulation. Through discussion and example in this paper, feasibility and validity of this method were proved, which may make foreshadowing for follow-up research work.

Hierarchical Bayesian Model Concerned Uncertainty in Number of Failure Events for Component Failure Rates

Components failure rates are important inputs of Probabilistic Risk Assessment (PRA). They are generally estimated from data observed in plant’s experiments. However, number of failure events derived from plant’s experiments might not represent the actual value. This means that true failure rates might have more uncertainty than one estimated from the actual experiments. Therefore it has been necessary to develop statistical Bayesian model which can reflect an uncertainty in data for components failure rates. In this study, the uncertainty in number of failure events is considered as a probability process and the hierarchical Bayesian model is applied to reflect the uncertainty for failure rates. As a result, more appropriate result of PRA is obtained with a state of knowledge about number of failure events for failure rates.