Application of numerical interval analysis to obtain self-validating results for multivariate probabilities in a massively parallel environment

Conventional computations use real numbers as input and produce real numbers as results without any indication of the accuracy. Interval analysis, instead, uses interval elements throughout the computation and produces intervals as output with the guarantee that the true results are contained in them. One major use for interval analysis in statistics is to get results of high-dimensional multivariate probabilities. With the efforts to decrease the length of the intervals that contain the theoretically true answers, we can obtain results to any arbitrary accuracy, which is demonstrated by multivariate normal and multivariate t integrations. This is an advantage over the approximation methods that are currently in use. Since interval analysis is more computationally intensive than traditional computing, a MasPar parallel computer is used in this research to improve performance.