PROBABILISTIC INFORMATION INTEGRATION TECHNOLOGY

The Statistical Sciences Group at Los Alamos has successfully developed a structured, probabilistic, quantitative approach for the evaluation of system performance based on multiple information sources, called Information Integration Technology (IIT). The technology integrates diverse types and sources of data and information (both quantitative and qualitative), and their associated uncertainties, to develop distributions for performance metrics, such as reliability. Applications include predicting complex system performance, where test data are lacking or expensive to obtain, through the integration of expert judgment, historical data, computer/simulation model predictions, and any relevant test/experimental data. The technology is particularly well suited for tracking estimated system performance for systems under change (e.g. development, aging), and can be used at any time during product development, including concept and early design phases, prior to prototyping, testing, or production, and before costly design decisions are made. Techniques from various disciplines (e.g., state-of-the-art expert elicitation, statistical and reliability analysis, design engineering, physics modeling, and knowledge management) are merged and modified to develop formal methods for the data/information integration. The power of this technology, known as PREDICT (Performance and Reliability Evaluation with Diverse Information Combination and Tracking), won a 1999 R and D 100 Award (Meyer, Booker, Bement, Kerscher, 1999). Specifically the PREDICT application is a formal, multidisciplinary process for estimating the performance of a product when test data are sparse or nonexistent. The acronym indicates the purpose of the methodology: to evaluate the performance or reliability of a product/system by combining all available (often diverse) sources of information and then tracking that performance as the product undergoes changes