P-boxes and probability bounds analysis have been used in many applications spanning many disciplines in engineering and environmental science, including:
Safety engineering is an engineering discipline which assures that engineered systems provide acceptable levels of safety. It is strongly related to industrial engineering/systems engineering, and the subset system safety engineering. Safety engineering assures that a life-critical system behaves as needed, even when components fail.
Risk assessment determines possible mishaps, their likelihood and consequences, and the tolerances for such events. The results of this process may be expressed in a quantitative or qualitative fashion. Risk assessment is an inherent part of a broader risk management strategy to help reduce any potential risk-related consequences.
Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system can be divided and allocated to different sources of uncertainty in its inputs. This involves estimating sensitivity indices that quantify the influence of an input or group of inputs on the output. A related practice is uncertainty analysis, which has a greater focus on uncertainty quantification and propagation of uncertainty; ideally, uncertainty and sensitivity analysis should be run in tandem.
Reliability engineering is a sub-discipline of systems engineering that emphasizes the ability of equipment to function without failure. Reliability is defined as the probability that a product, system, or service will perform its intended function adequately for a specified period of time, OR will operate in a defined environment without failure. Reliability is closely related to availability, which is typically described as the ability of a component or system to function at a specified moment or interval of time.
Probabilistic risk assessment (PRA) is a systematic and comprehensive methodology to evaluate risks associated with a complex engineered technological entity or the effects of stressors on the environment.
Info-gap decision theory seeks to optimize robustness to failure under severe uncertainty, in particular applying sensitivity analysis of the stability radius type to perturbations in the value of a given estimate of the parameter of interest. It has some connections with Wald's maximin model; some authors distinguish them, others consider them instances of the same principle.
Uncertainty quantification (UQ) is the science of quantitative characterization and estimation of uncertainties in both computational and real world applications. It tries to determine how likely certain outcomes are if some aspects of the system are not exactly known. An example would be to predict the acceleration of a human body in a head-on crash with another car: even if the speed was exactly known, small differences in the manufacturing of individual cars, how tightly every bolt has been tightened, etc., will lead to different results that can only be predicted in a statistical sense.
Probabilistic design is a discipline within engineering design. It deals primarily with the consideration and minimization of the effects of random variability upon the performance of an engineering system during the design phase. Typically, these effects studied and optimized are related to quality and reliability. It differs from the classical approach to design by assuming a small probability of failure instead of using the safety factor. Probabilistic design is used in a variety of different applications to assess the likelihood of failure. Disciplines which extensively use probabilistic design principles include product design, quality control, systems engineering, machine design, civil engineering and manufacturing.
In science, engineering, and research, expert elicitation is the synthesis of opinions of authorities of a subject where there is uncertainty due to insufficient data or when such data is unattainable because of physical constraints or lack of resources. Expert elicitation is essentially a scientific consensus methodology. It is often used in the study of rare events. Expert elicitation allows for parametrization, an "educated guess", for the respective topic under study. Expert elicitation generally helps quantify uncertainty.
Quantification of Margins and Uncertainty (QMU) is a decision support methodology for complex technical decisions. QMU focuses on the identification, characterization, and analysis of performance thresholds and their associated margins for engineering systems that are evaluated under conditions of uncertainty, particularly when portions of those results are generated using computational modeling and simulation. QMU has traditionally been applied to complex systems where comprehensive experimental test data is not readily available and cannot be easily generated for either end-to-end system execution or for specific subsystems of interest. Examples of systems where QMU has been applied include nuclear weapons performance, qualification, and stockpile assessment. QMU focuses on characterizing in detail the various sources of uncertainty that exist in a model, thus allowing the uncertainty in the system response output variables to be well quantified. These sources are frequently described in terms of probability distributions to account for the stochastic nature of complex engineering systems. The characterization of uncertainty supports comparisons of design margins for key system performance metrics to the uncertainty associated with their calculation by the model. QMU supports risk-informed decision-making processes where computational simulation results provide one of several inputs to the decision-making authority. There is currently no standardized methodology across the simulation community for conducting QMU; the term is applied to a variety of different modeling and simulation techniques that focus on rigorously quantifying model uncertainty in order to support comparison to design margins.
NESSUS is a general-purpose, probabilistic analysis program that simulates variations and uncertainties in loads, geometry, material behavior and other user-defined inputs to compute probability of failure and probabilistic sensitivity measures of engineered systems. Because NESSUS uses highly efficient and accurate probabilistic analysis methods, probabilistic solutions can be obtained even for extremely large and complex models. The system performance can be hierarchically decomposed into multiple smaller models and/or analytical equations. Once the probabilistic response is quantified, the results can be used to support risk-informed decisions regarding reliability for safety critical and one-of-a-kind systems, and to maintain a level of quality while reducing manufacturing costs for larger quantity products.
Vladik Kreinovich is a professor of computer science at the University of Texas at El Paso.
A probability box is a characterization of uncertain numbers consisting of both aleatoric and epistemic uncertainties that is often used in risk analysis or quantitative uncertainty modeling where numerical calculations must be performed. Probability bounds analysis is used to make arithmetic and logical calculations with p-boxes.
Analytica is a visual software developed by Lumina Decision Systems for creating, analyzing and communicating quantitative decision models. It combines hierarchical influence diagrams for visual creation and view of models, intelligent arrays for working with multidimensional data, Monte Carlo simulation for analyzing risk and uncertainty, and optimization, including linear and nonlinear programming. Its design is based on ideas from the field of decision analysis. As a computer language, it combines a declarative (non-procedural) structure for referential transparency, array abstraction, and automatic dependency maintenance for efficient sequencing of computation.
Probability bounds analysis (PBA) is a collection of methods of uncertainty propagation for making qualitative and quantitative calculations in the face of uncertainties of various kinds. It is used to project partial information about random variables and other quantities through mathematical expressions. For instance, it computes sure bounds on the distribution of a sum, product, or more complex function, given only sure bounds on the distributions of the inputs. Such bounds are called probability boxes, and constrain cumulative probability distributions.
Expert Judgment (EJ) denotes a wide variety of techniques ranging from a single undocumented opinion, through preference surveys, to formal elicitation with external validation of expert probability assessments. Recent books are . In the nuclear safety area, Rasmussen formalized EJ by documenting all steps in the expert elicitation process for scientific review. This made visible wide spreads in expert assessments and teed up questions regarding the validation and synthesis of expert judgments. The nuclear safety community later took onboard expert judgment techniques underpinned by external validation . Empirical validation is the hallmark of science, and forms the centerpiece of the classical model of probabilistic forecasting . A European Network coordinates workshops. Application areas include nuclear safety, investment banking, volcanology, public health, ecology, engineering, climate change and aeronautics/aerospace. For a survey of applications through 2006 see and give exhortatory overviews. A recent large scale implementation by the World Health Organization is described in . A long running application at the Montserrat Volcano Observatory is described in . The classical model scores expert performance in terms of statistical accuracy and informativeness . These terms should not be confused with “accuracy and precision”. Accuracy “is a description of systematic errors” while precision “is a description of random errors”. In the classical model statistical accuracy is measured as the p-value or probability with which one would falsely reject the hypotheses that an expert's probability assessments were statistically accurate. A low value means it is very unlikely that the discrepancy between an expert's probability statements and observed outcomes should arise by chance. Informativeness is measured as Shannon relative information with respect to an analyst-supplied background measure. Shannon relative information is used because it is scale invariant, tail insensitive, slow, and familiar. Parenthetically, measures with physical dimensions, such as the standard deviation, or the width of prediction intervals, raise serious problems, as a change of units would affect some variables but not others. The product of statistical accuracy and informativeness for each expert is their combined score. With an optimal choice of a statistical accuracy threshold beneath which experts are unweighted, the combined score is a long run “strictly proper scoring rule”: an expert achieves his long run maximal expected score by and only by stating his true beliefs. The classical model derives Performance Weighted (PW) combinations. These are compared with Equally Weighted (EW) combinations, and recently with Harmonically Weighted (HW) combinations, as well as with individual expert assessments.
NUSAP is a notational system for the management and communication of uncertainty in science for policy, based on five categories for characterizing any quantitative statement: Numeral, Unit, Spread, Assessment and Pedigree. NUSAP was introduced by Silvio Funtowicz and Jerome Ravetz in the 1990 book Uncertainty and Quality in Science for Policy. See also van der Sluijs et al. 2005.
Scott David Ferson is Chair of Uncertainty in Engineering at University of Liverpool, Professor in its School of Engineering, and director of the Institute for Risk and Uncertainty there. Before joining the University of Liverpool, Ferson taught as an adjunct professor at Stony Brook University and did research at Applied Biomathematics, a small think tank on Long Island, New York. He was named a Fellow of the Society for Risk Analysis and received its Distinguished Educator Award in 2017. From Shelbyville, Indiana, Ferson received a PhD from Stony Brook University and an A.B. from Wabash College.
Line sampling is a method used in reliability engineering to compute small failure probabilities encountered in engineering systems. The method is particularly suitable for high-dimensional reliability problems, in which the performance function exhibits moderate non-linearity with respect to the uncertain parameters The method is suitable for analyzing black box systems, and unlike the importance sampling method of variance reduction, does not require detailed knowledge of the system.
In regression analysis, an interval predictor model (IPM) is an approach to regression where bounds on the function to be approximated are obtained. This differs from other techniques in machine learning, where usually one wishes to estimate point values or an entire probability distribution. Interval Predictor Models are sometimes referred to as a nonparametric regression technique, because a potentially infinite set of functions are contained by the IPM, and no specific distribution is implied for the regressed variables.
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