Bayesian Analysis
Bayesian analysis is an integral part of the 217Plus and PRISM modeling methodologies. Bayesian analysis refers to the use of test and field failure data to adjust the failure rates calculated for an assembly. The empirical data in a Prediction Bayesian file is used to adjust the base failure rate calculated for the assembly so that it is more representative of your individual application.
The benefits of using test and field data to adjust a calculated failure rate are:
• Integration of current reliability data when the failure rate prediction is performed.
• Optimization of the prediction based on valid historical data.
Bayesian analysis accounts for the quantity of test and field data by weighting large data sets more heavily than small data sets. When performing Bayesian analysis, you should collect as much test and field data as possible to support the prediction. This includes the use of zero failure data because having no failures over a given time period is equally as important to the determination of the estimated failure rate of an item as data where failures are experienced. The application of data is explained in
Bayesian Analysis Precautions.