When the EB method is used in complex models with many parameters, it often requires the computation of multidimensional definite integrals. This requirement has effectively placed practical limits on the complexity of the problems a long time in the past. However, the parallel development of a Monte Carlo simulation based numerical strategies have revolutionized the applied Bayesian data analysis with the availability of super computing power over the past number of decades. These strategies provide a structure within which many complex Bayesian models can be analyzed using generic software.
Moreover, the simulation study is a tractable practice that can be utilized to examine and compare the performance of different estimators empirically. In this study, one generates a sample of random data in such a way that mimics a real problem and recapitulates …show more content…
It is the most widely used quantitative technique because of higher flexibility and easier understandable ability and yet powerful enough to yield ample number of constructive results even when fitting is highly structured.
1.9 LITERATURE REVIEW
Most of the engineering designs are more evolutionary rather than revolutionary processes in which current equipment is modified to suit the new requirements. In such instances, it would appear that the known facts regarding reliability of the current hardware could possibly be utilized in an attempt to improve the quality of the reliability estimators of the new design. The sampling theory methods are inappropriate for incorporating such related information. In such situation, the EB method produces believable results, which goes a long way in convincing the engineer for the utility of EB approach. From a practical perspective, this approach is becoming more and more attractive, mainly because of the fast developments in computing and the introduction of efficient numerical algorithms for carrying out