Propagation of Imprecise Probabilities through Black Box Models

From the decision-based design perspective, decision making is the critical element of the design process. All practical decision making occurs under some degree of uncertainty. Subjective expected utility theory is a well-established method for decision making under uncertainty; however, it assumes that the DM can express his or her beliefs as precise probability distributions. For many reasons, both practical and theoretical, it can be beneficial to relax this assumption of precision. One possible means for avoiding this assumption is the use of imprecise probabilities. Imprecise probabilities are more expressive of uncertainty than precise probabilities, but they are also more computationally cumbersome. Probability Bounds Analysis (PBA) is a compromise between the expressivity of imprecise probabilities and the computational ease of modeling beliefs with precise probabilities. In order for PBA to be implemented in engineering design, it is necessary to develop appropriate computational methods for propagating probability boxes (p-boxes) through black box engineering models. This thesis examines the range of applicability of current methods for p-box propagation and proposes three alternative methods. These methods are applied towards the solution of three successively complex numerical examples.M.S.Committee Chair: Paredis, Chris; Committee Member: Bras, Bert; Committee Member: McGinnis, Leo

Dependence in probabilistic modeling, Dempster-Shafer theory, and probability bounds analysis.

This report summarizes methods to incorporate information (or lack of information) about inter-variable dependence into risk assessments that use Dempster-Shafer theory or probability bounds analysis to address epistemic and aleatory uncertainty. The report reviews techniques for simulating correlated variates for a given correlation measure and dependence model, computation of bounds on distribution functions under a specified dependence model, formulation of parametric and empirical dependence models, and bounding approaches that can be used when information about the intervariable dependence is incomplete. The report also reviews several of the most pervasive and dangerous myths among risk analysts about dependence in probabilistic models

Experimental uncertainty estimation and statistics for data having interval uncertainty.

This report addresses the characterization of measurements that include epistemic uncertainties in the form of intervals. It reviews the application of basic descriptive statistics to data sets which contain intervals rather than exclusively point estimates. It describes algorithms to compute various means, the median and other percentiles, variance, interquartile range, moments, confidence limits, and other important statistics and summarizes the computability of these statistics as a function of sample size and characteristics of the intervals in the data (degree of overlap, size and regularity of widths, etc.). It also reviews the prospects for analyzing such data sets with the methods of inferential statistics such as outlier detection and regressions. The report explores the tradeoff between measurement precision and sample size in statistical results that are sensitive to both. It also argues that an approach based on interval statistics could be a reasonable alternative to current standard methods for evaluating, expressing and propagating measurement uncertainties