Applications of p-boxes and probability bounds analysis

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P-boxes and probability bounds analysis have been used in many applications spanning many disciplines in engineering and environmental science, including:

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<span class="mw-page-title-main">Risk assessment</span> Estimation of risk associated with exposure to a given set of hazards

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.

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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.

<span class="mw-page-title-main">Vladik Kreinovich</span> American computer scientist

Vladik Kreinovich is a professor of computer science at the University of Texas at El Paso.

<span class="mw-page-title-main">Probability box</span> Characterization of uncertain numbers consisting of both aleatoric and epistemic uncertainties

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.

References

  1. Aughenbaugh, J. M., and C.J.J. Paredis (2007). Probability bounds analysis as a general approach to sensitivity analysis in decision making under uncertainty Archived 2012-03-21 at the Wayback Machine . SAE 2007 Transactions Journal of Passenger Cars: Mechanical Systems, (Section 6)116: 1325–1339, SAE International, Warrendale, Pennsylvania.
  2. Flander, L., W. Dixon, M. McBride, and M. Burgman. (2012). Facilitated expert judgment of environmental risks: acquiring and analysing imprecise data. International Journal of Risk Assessment and Management16: 199–212.
  3. Dixon, W.J. (2007). The use of Probability Bounds Analysis for Characterising and Propagating Uncertainty in Species Sensitivity Distributions. Technical Report Series No. 163, Arthur Rylah Institute for Environmental Research, Department of Sustainability and Environment. Heidelberg, Victoria, Australia.
  4. Oberguggenberger, M., J. King and B. Schmelzer (2007). Imprecise probability methods for sensitivity analysis in engineering. Proceedings of the 5th International Symposium on Imprecise Probability: Theories and Applications, Prague, Czech Republic.
  5. Enszer, J.A., Y. Lin, S. Ferson, G.F. Corliss and M.A. Stadtherr (2011). Probability bounds analysis for nonlinear dynamic process models. AIChE Journal57: 404–422.
  6. Enszer, Joshua Alan, (2010). Verified Probability Bound Analysis for Dynamic Nonlinear Systems. Dissertation, University of Notre Dame.
  7. Nong, A., and K. Krishnan (2007). Estimation of interindividual pharmacokinetic variability factor for inhaled volatile organic chemicals using a probability-bounds approach. Regulatory Toxicology and Pharmacology 48: 93–101.
  8. Guyonnet, D., F. Blanchard, C. Harpet, Y. Ménard, B. Côme and C. Baudrit (2005). Projet IREA—Traitement des incertitudes en évaluation des risques d'exposition, Annexe B, Cas «Eaux souterraines». Rapport BRGM/RP-54099-FR, Bureau de Recherches Géologiques et Minières, France. Archived 2012-03-11 at the Wayback Machine
  9. Fetz, Thomas; Tonon, Fulvio (2008). "Probability bounds for series systems with variables constrained by sets of probability measures". International Journal of Reliability and Safety. 2 (4): 309. doi:10.1504/IJRS.2008.022079.
  10. 1 2 Augustsson, A., M. Filipsson, T. Öberg, B. Bergbäck (2011). Climate change—an uncertainty factor in risk analysis of contaminated land. Science of the Total Environment409: 4693–4700.
  11. Baudrit, C., D. Guyonnet, H. Baroudi, S. Denys and P. Begassat (2005). Assessment of child exposure to lead on an ironworks brownfield: uncertainty analysis. 9th International FZK/TNO Conference on Contaminated Soil – ConSoil2005, Bordeaux, France, pages 1071–1080.
  12. Dixon, W.J. (2007). Uncertainty Propagation in Population Level Salinity Risk Models. Technical Report Technical Report Series No. 164, Arthur Rylah Institute for Environmental Research. Heidelberg, Victoria, Australia
  13. Karanki, D.R., H.S. Kushwaha, A.K. Verma, and S. Ajit. (2009). Uncertainty analysis based on probability bounds (p-box) approach in probabilistic safety assessment. Risk Analysis29: 662–75.
  14. Sander, P., B. Bergbäck and T. Öberg (2006). Uncertain numbers and uncertainty in the selection of input distributions—Consequences for a probabilistic risk assessment of contaminated land. Risk Analysis26: 1363–1375.
  15. Minnery, J.G., J.G. Jacangelo, L.I. Boden, D.J. Vorhees and W. Heiger-Bernays (2009). Sensitivity analysis of the pressure-based direct integrity test for membranes used in drinking water treatment. Environmental Science and Technology43(24): 9419–9424.
  16. Regan, H.M., B.E. Sample and S. Ferson (2002). Comparison of deterministic and probabilistic calculation of ecological soil screening levels. Environmental Toxicology and Chemistry 21: 882–890.
  17. U.S. Environmental Protection Agency (Region I), GE/Housatonic River Site in New England
  18. Moore, Dwayne R.J.; Breton, Roger L.; Delong, Tod R.; Ferson, Scott; Lortie, John P.; MacDonald, Drew B.; McGrath, Richard; Pawlisz, Andrzej; Svirsky, Susan C.; Teed, R. Scott; Thompson, Ryan P.; Whitfield Aslund, Melissa (2016). "Ecological risk assessment for mink and short-tailed shrew exposed to PCBS, dioxins, and furans in the Housatonic River area". Integrated Environmental Assessment and Management. 12 (1): 174–184. Bibcode:2016IEAM...12..174M. doi:10.1002/ieam.1661. PMID   25976918.
  19. U.S. Environmental Protection Agency (Region 6 Superfund Program), Calcasieu Estuary Remedial Investigation Archived January 20, 2011, at the Wayback Machine
  20. Roy, C.J., and M.S. Balch (2012). A holistic approach to uncertainty quantification with application to supersonic nozzle thrust. International Journal for Uncertainty Quantification2: 363-381. doi : 10.1615/Int.J.UncertaintyQuantification.2012003562.
  21. Oberkampf, W.L., and C. J. Roy. (2010). Verification and Validation in Scientific Computing. Cambridge University Press.
  22. Regan, H.M., B.K. Hope, and S. Ferson (2002). Analysis and portrayal of uncertainty in a food web exposure model. Human and Ecological Risk Assessment8: 1757–1777.
  23. Ferson, S., and W.T. Tucker (2004). Reliability of risk analyses for contaminated groundwater. Groundwater Quality Modeling and Management under Uncertainty, edited by S. Mishra, American Society of Civil Engineers Reston, VA.
  24. Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P. (2013). "Reliability analysis of polynomial systems subject to p-box uncertainties". Mechanical Systems and Signal Processing. 37 (1–2): 121–136. Bibcode:2013MSSP...37..121C. doi:10.1016/j.ymssp.2012.08.012.
  25. Ferson, Scott; Burgman, Mark A. (1995). "Correlations, dependency bounds and extinction risks". Biological Conservation. 73 (2): 101–105. Bibcode:1995BCons..73..101F. doi:10.1016/0006-3207(95)90031-4.
  26. Ferson, S., D.R.J. Moore, P.J. Van den Brink, T.L. Estes, K. Gallagher, R. O'Connor and F. Verdonck. (2010). Bounding uncertainty analyses. Pages 89–122 in Application of Uncertainty Analysis to Ecological Risks of Pesticides, edited by W. J. Warren-Hicks and A. Hart. CRC Press, Boca Raton, Florida.
  27. Kriegler, E., and H. Held (2005). Utilizing belief functions for the estimation of future climate change. International Journal of Approximate Reasoning39: 185–209.
  28. Kriegler, E. (2005). Imprecise probability analysis for integrated assessment of climate change, Ph.D. dissertation, Universität Potsdam, Germany.
  29. Batarseh, O.G.Y., (2010). An Interval Based Approach to Model Input Uncertainty in Discrete-event Simulation. Ph.D. dissertation, University of Central Florida.
  30. Goldwasser, L., L. Ginzburg and S. Ferson (2000). Variability and measurement error in extinction risk analysis: the northern spotted owl on the Olympic Peninsula. Pages 169–187 in Quantitative Methods for Conservation Biology, edited by S. Ferson and M. Burgman, Springer-Verlag, New York.
  31. Hayes, K.R. (2011).Uncertainty and uncertainty analysis methods: Issues in quantitative and qualitative risk modeling with application to import risk assessment ACERA project (0705). Report Number: EP102467, CSIRO, Hobart, Australia.
  32. Zhang, H., R.L. Mullen, and R.L. Muhanna (2010). Finite element structural analysis using imprecise probabilities based on p-box representation. Proceedings of the 4th International Workshop on Reliable Engineering Computing (REC 2010).
  33. Zhang, H., R. Mullen, R. Muhanna (2012). Safety Structural Analysis with Probability-Boxes. International Journal of Reliability and Safety6: 110–129.
  34. Patelli, E; de Angelis, M (2015). "Line sampling approach for extreme case analysis in presence of aleatory and epistemic uncertainties". Safety and Reliability of Complex Engineered Systems. pp. 2585–2593. doi:10.1201/b19094-339 (inactive 2024-11-11). ISBN   978-1-138-02879-1.{{cite book}}: CS1 maint: DOI inactive as of November 2024 (link)
  35. Mehl, Christopher H. (2013). "P-boxes for cost uncertainty analysis". Mechanical Systems and Signal Processing. 37 (1–2): 253–263. Bibcode:2013MSSP...37..253M. doi:10.1016/j.ymssp.2012.03.014.
  36. Sentz, K., and S. Ferson (2011). Probabilistic bounding analysis in the quantification of margins and uncertainties. Reliability Engineering and System Safety96: 1126–1136.
  37. Rozell, Daniel J., and Sheldon J. Reaven (2012). Water pollution risk associated with natural gas extraction from the Marcellus Shale. Risk Analysis32: 1382–1393.
  38. Graça Marto, Simão and Vasile, Massimiliano and Epenoy, Richard (2019) Multi-objective robust trajectory optimisation under epistemic uncertainty and imprecision. In: 70th International Astronautical Congress, 2019-10-21 - 2019-10-25
  39. Vasile, Massimiliano; Tardioli, Chiara (2018). "On the Use of Positive Polynomials for the Estimation of Upper and Lower Expectations in Orbital Dynamics". Stardust Final Conference. Astrophysics and Space Science Proceedings. Vol. 52. pp. 99–107. doi:10.1007/978-3-319-69956-1_6. ISBN   978-3-319-69955-4.