Relevant bibliographies by topics / Imprecise Probability

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Imprecise Probability'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Imprecise Probability"

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de Cooman, Gert, and Filip Hermans. "Imprecise probability trees: Bridging two theories of imprecise probability." Artificial Intelligence 172, no. 11 (2008): 1400–1427. http://dx.doi.org/10.1016/j.artint.2008.03.001.

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Coolen, Frank, Thomas Fetz, Serafín Moral, and Michael Oberguggenberger. "Editorial – Imprecise probability." International Journal of Approximate Reasoning 53, no. 8 (2012): 1107–9. http://dx.doi.org/10.1016/j.ijar.2012.06.016.

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Peressini, Anthony F. "Imprecise Probability and Chance." Erkenntnis 81, no. 3 (2015): 561–86. http://dx.doi.org/10.1007/s10670-015-9755-9.

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Zaffalon, Marco. "Credibility via imprecise probability." International Journal of Approximate Reasoning 39, no. 2-3 (2005): 115–21. http://dx.doi.org/10.1016/j.ijar.2004.11.001.

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Kozine, Igor O., and Lev V. Utkin. "Constructing imprecise probability distributions." International Journal of General Systems 34, no. 4 (2005): 401–8. http://dx.doi.org/10.1080/03081070500201701.

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Ding, Yifeng, Wesley H. Holliday, and Thomas F. Icard. "Logics of imprecise comparative probability." International Journal of Approximate Reasoning 132 (May 2021): 154–80. http://dx.doi.org/10.1016/j.ijar.2021.02.004.

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Ferson, Scott, and William L. Oberkampf. "Validation of imprecise probability models." International Journal of Reliability and Safety 3, no. 1/2/3 (2009): 3. http://dx.doi.org/10.1504/ijrs.2009.026832.

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Augustin, Thomas, Serena Doria, and Mássimo Marinacci. "Imprecise probability: Theories and applications." International Journal of Approximate Reasoning 84 (May 2017): 39–40. http://dx.doi.org/10.1016/j.ijar.2017.03.001.

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Regoli, G. "Inference under imprecise probability assessments." Soft Computing - A Fusion of Foundations, Methodologies and Applications 3, no. 3 (1999): 181–86. http://dx.doi.org/10.1007/s005000050067.

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Galvan, Bruno. "Quantum Mechanics and Imprecise Probability." Journal of Statistical Physics 131, no. 6 (2008): 1155–67. http://dx.doi.org/10.1007/s10955-008-9530-2.

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Dissertations / Theses on the topic "Imprecise Probability"

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RENTERIA, ALEXANDRE ROBERTO. "FUZZY PROBABILITY ESTIMATION FROM IMPRECISE DATA." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2006. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=9815@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>Existem três tipos de incerteza: a de natureza aleatória, a gerada pelo conhecimento incompleto e a que ocorre em função do conhecimento vago ou impreciso. Há casos em que dois tipos de incerteza estão presentes, em especial nos experimentos aleatórios a partir de dados imprecisos. Para modelar a aleatoriedade quando a distribuição de probabilidade que rege o experimento não é conhecida, deve-se utilizar um método de estimação nãoparamétrico, tal como a janela de Parzen. Já a incerteza de medição, presente em qualquer medida de uma gr
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Kriegler, Elmar. "Imprecise probability analysis for integrated assessment of climate change." Phd thesis, [S.l. : s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=976700247.

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Elkin, Lee [Verfasser], and Stephan [Akademischer Betreuer] Hartmann. "Imprecise probability in epistemology / Lee Elkin ; Betreuer: Stephan Hartmann." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2017. http://d-nb.info/1142787079/34.

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Elkin, Lee Verfasser], and Stephan [Akademischer Betreuer] [Hartmann. "Imprecise probability in epistemology / Lee Elkin ; Betreuer: Stephan Hartmann." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2017. http://nbn-resolving.de/urn:nbn:de:bvb:19-210424.

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Ha, Cong Loc. "Time-dependent reliability analysis for deteriorating structures using imprecise probability theory." Thesis, The University of Sydney, 2017. http://hdl.handle.net/2123/17731.

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Reliability analysis, which takes into account uncertainties, is considered to be the best tool for modern structural evaluation. In this assessment, the deterioration model is one of the most important factors, but it is complicated for modelling due to the inherent uncertainties in the deterioration process. Theoretically, the uncertainties of the deterioration process can be modelled using a probabilistic approach. However, there are practical difficulties in identifying the probabilistic model for the deterioration process as the actual deterioration data are rather limited. Also, the depe
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Balch, Michael Scott. "Methods for Rigorous Uncertainty Quantification with Application to a Mars Atmosphere Model." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/30115.

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The purpose of this dissertation is to develop and demonstrate methods appropriate for the quantification and propagation of uncertainty in large, high-consequence engineering projects. The term "rigorous uncertainty quantification" refers to methods equal to the proposed task. The motivating practical example is uncertainty in a Mars atmosphere model due to the incompletely characterized presence of dust. The contributions made in this dissertation, though primarily mathematical and philosophical, are driven by the immediate needs of engineers applying uncertainty quantification in the field
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Idefeldt, Jim. "An applied approach to numerically imprecise decision making." Doctoral thesis, Mittuniversitetet, Institutionen för informationsteknologi och medier, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-7147.

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Despite the fact that unguided decision making might lead to inefficient and nonoptimal decisions, decisions made at organizational levels seldom utilise decisionanalytical tools. Several gaps between the decision-makers and the computer baseddecision tools exist, and a main problem in managerial decision-making involves the lack of information and precise objective data, i.e. uncertainty and imprecision may be inherent in the decision situation. We believe that this problem might be overcome by providing computer based decision tools capable of handling the uncertainty inherent in real-life d
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Feng, G. "Efficient reliability and sensitivity analysis of complex systems and networks with imprecise probability." Thesis, University of Liverpool, 2017. http://livrepository.liverpool.ac.uk/3009365/.

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Complex systems and networks, such as grid systems and transportation networks, are backbones of our society, so performing RAMS (Reliability, Availability, Maintainability, and Safety) analysis on them is essential. The complex system consists of multiple component types, which is time consuming to analyse by using cut sets or system signatures methods. Analytical solutions (when available) are always preferable than simulation methods since the computational time is in general negligible. However, analytical solutions are not always available or are restricted to particular cases. For instan
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Ling, Jay Michael. "Managing Information Collection in Simulation-Based Design." Thesis, Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/11504.

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An important element of successful engineering design is the effective management of resources to support design decisions. Design decisions can be thought of as having two phasesa formulation phase and a solution phase. As part of the formulation phase, engineers must decide how much information to collect and which models to use to support the design decision. Since more information and more accurate models come at a greater cost, a cost-benefit trade-off must be made. Previous work has considered such trade-offs in decision problems when all aspects of the decision problem can be repres
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Batarseh, Ola. "AN INTERVAL BASED APPROACH TO MODEL INPUT UNCERTAINTY IN DISCRETE-EVENT SIMULATION." Doctoral diss., University of Central Florida, 2010. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2540.

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The objective of this research is to increase the robustness of discrete-event simulation (DES) when input uncertainties associated models and parameters are present. Input uncertainties in simulation have different sources, including lack of data, conflicting information and beliefs, lack of introspection, measurement errors, and lack of information about dependency. A reliable solution is obtained from a simulation mechanism that accounts for these uncertainty components in simulation. An interval-based simulation (IBS) mechanism based on imprecise probabilities is proposed, where the statis
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Books on the topic "Imprecise Probability"

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Austria) International Symposium on Imprecise Probabilities and Their Applications (7th 2011 Innsbruck. ISIPTA '11: Proceedings of the Seventh International Symposium on Imprecise Probability, Theories and Applications : July 25-28, 2011, Innsbruck, Austria. SIPTA, Society for Imprecise Probability, Theories and Applications, 2011.

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Walley, Peter. Statistical reasoning with imprecise probabilities. Chapman and Hall, 1991.

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Coolen, Frank P., Gert De Cooman, Matthias C. Troffaes, and Thomas Augustin. Introduction to Imprecise Probabilities. Wiley & Sons, Incorporated, John, 2014.

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Troffaes, Matthias C. M., Gert de Cooman, Thomas Augustin, and Frank P. A. Coolen. Introduction to Imprecise Probabilities. Wiley & Sons, Limited, John, 2014.

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Introduction to Imprecise Probabilities. Wiley, 2014.

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Cozman, Fabio. Imprecise and Indeterminate Probabilities. Edited by Alan Hájek and Christopher Hitchcock. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199607617.013.14.

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This chapter offers a discussion of imprecision and indeterminacy in probability values; that is, there is a focus on situations where one does not attach a single real number to every possible event. There are several theories and mathematical models regarding such imprecise and indeterminate probabilities. Among these are, for instance, lower/upper or interval probabilities or expectations, Choquet capacities, belief functions and credal sets. The chapter reviews the history of some of these models and theories, and then summarizes their main technical assumptions. Some subtleties concerning
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Book chapters on the topic "Imprecise Probability"

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Coolen, Frank P. A., Matthias C. M. Troffaes, and Thomas Augustin. "Imprecise Probability." In International Encyclopedia of Statistical Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_296.

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Coolen, Frank P. A., Matthias C. M. Troffaes, and Thomas Augustin. "Imprecise Probability." In International Encyclopedia of Statistical Science. Springer Berlin Heidelberg, 2025. https://doi.org/10.1007/978-3-662-69359-9_278.

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Vovk, Vladimir, and Glenn Shafer. "Game-theoretic probability." In Introduction to Imprecise Probabilities. John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118763117.ch6.

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Pelessoni, Renato, and Paolo Vicig. "Probability Inequalities with Imprecise Previsions." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-74000-8_6.

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Wheeler, Gregory. "A Gentle Approach to Imprecise Probability." In Theory and Decision Library A:. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15436-2_3.

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Quaeghebeur, Erik. "Introduction to the Theory of Imprecise Probability." In Uncertainty in Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83640-5_3.

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AbstractThe theory of imprecise probability is a generalization of classical ‘precise’ probability theory that allows modeling imprecision and indecision. This is a practical advantage in situations where a unique precise uncertainty model cannot be justified. This arises, for example, when there is a relatively small amount of data available to learn the uncertainty model or when the model’s structure cannot be defined uniquely. The tools the theory provides make it possible to draw conclusions and make decisions that correctly reflect the limited information or knowledge available for the un
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Krpelik, Daniel, Frank P. A. Coolen, and Louis J. M. Aslett. "Imprecise Probability Inference on Masked Multicomponent System." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97547-4_18.

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Jacob, Christelle, Didier Dubois, and Janette Cardoso. "From Imprecise Probability Laws to Fault Tree Analysis." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33362-0_40.

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Moral, Serafín. "Imprecise Probability in Graphical Models: Achievements and Challenges." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11518655_1.

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Gilio, Angelo. "Algorithms for Precise and Imprecise Conditional Probability Assessments." In Mathematical Models for Handling Partial Knowledge in Artificial Intelligence. Springer US, 1995. http://dx.doi.org/10.1007/978-1-4899-1424-8_15.

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Conference papers on the topic "Imprecise Probability"

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Wei, Pengfei, Jingwen Song, Marcos A. Valdebenito, and Michael Beer. "Efficient Propagation of Imprecise Probability Models by Imprecise line Sampling." In Proceedings of the 29th European Safety and Reliability Conference (ESREL). Research Publishing Services, 2019. http://dx.doi.org/10.3850/978-981-11-2724-3_0994-cd.

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Bruns, Morgan, and Christiaan J. J. Paredis. "Numerical Methods for Propagating Imprecise Uncertainty." In ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/detc2006-99237.

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Since engineering design requires decision making under uncertainty, the degree to which good decisions can be made depends upon the degree to which the decision maker has expressive and accurate representations of his or her uncertain beliefs. Whereas traditional decision analysis uses precise probability distributions to represent uncertain beliefs, recent research has examined the effects of relaxing this assumption of precision. A specific example of this is the theory of imprecise probability. Imprecise probabilities are more expressive than precise probabilities, but they are also more c
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Liu, Zheng, Hong-Zhong Huang, Liping He, Zhonglai Wang, and Ning-cong Xiao. "Reliability Analysis of Automotive Semi-Axle on Basic of Imprecise Probability." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12933.

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Most reliability models in the classical reliability theory use probability theory to quantify information under uncertainty and these models have a high demand of available data. While in practical applications, data related to system sand components are usually sparse or incomplete, moreover, some quantification of uncertainty should be based on subjective information and moreover subjective information may often be expressed in natural language. In these cases, classical reliability theory can not provide an effective way for the reliability analysis of systems. As a generalization of proba
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IMANOV, G. C. "IMPRECISE PROBABILITY FOR ESTIMATED FORECASTING VARIANTS OF THE ECONOMICAL DEVELOPMENT." In Proceedings of the MS'10 International Conference. WORLD SCIENTIFIC, 2010. http://dx.doi.org/10.1142/9789814324441_0003.

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IMANOV, G. C., H. S. ALIYEVA, and R. A. YUSIFZADE. "IMPRECISE PROBABILITY FOR ESTIMATED FORECASTING VARIANTS OF THE ECONOMICAL DEVELOPMENT." In Proceedings of the MS'10 International Conference. WORLD SCIENTIFIC, 2010. http://dx.doi.org/10.1142/9789814324441_0055.

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Bo, Yifan, Ming Yang, Xiangjun Zeng, Yu Zhou, and Linguo Jing. "Multi-fault Diagnosis of Wind Turbine Based on Imprecise Probability." In 2020 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia). IEEE, 2020. http://dx.doi.org/10.1109/icpsasia48933.2020.9208416.

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Liu, Zheng, Yan-Feng Li, Yuan-Jian Yang, Jinhua Mi, and Hong-Zhong Huang. "Extensions of Bayesian Reliability Analysis by Using Imprecise Dirichlet Model." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-47183.

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Bayesian approaches have been demonstrated as effective methods for reliability analysis of complex systems with small-amount data, which integrate prior information and sample data using Bayes’ theorem. However, there is an assumption that precise prior probability distributions are available for unknown parameters, yet these prior distributions are sometimes unavailable in practical engineering. A possible way to avoiding this assumption is to generalize Bayesian reliability analysis approach by using imprecise probability theory. In this paper, we adopt a set of imprecise Dirichlet distribu
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Ranade, A. K., M. Pandey, S. Paul, V. Suman, Brij Kumar, and D. Datta. "Risk analysis of ingestion dose through food chain using imprecise probability." In 2010 2nd International Conference on Reliability, Safety and Hazard - Risk-Based Technologies and Physics-of-Failure Methods (ICRESH). IEEE, 2010. http://dx.doi.org/10.1109/icresh.2010.5779620.

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Sanchez, Luciano, Ines Couso, and Jorge Casillas. "A Multiobjective Genetic Fuzzy System with Imprecise Probability Fitness for Vague Data." In 2006 International Symposium on Evolving Fuzzy Systems. IEEE, 2006. http://dx.doi.org/10.1109/isefs.2006.251156.

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Tabia, Karim. "Updating Probability Intervals with Uncertain Inputs." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/381.

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Probability intervals provide an intuitive, powerful and unifying setting for encoding and reasoning with imprecise beliefs. This paper addresses the problem of updating uncertain information specified in the form of probability intervals with new uncertain inputs also expressed as probability intervals. We place ourselves in the framework of Jeffrey's rule of conditioning and propose extensions of this conditioning for the interval-based setting. More precisely, we first extend Jeffrey's rule to credal sets then propose extensions of Jeffrey's rule to three common conditioning rules for proba
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Reports on the topic "Imprecise Probability"

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Picard, Richard Roy, and Scott Alan Vander Wiel. Imprecise Probability Methods for Weapons UQ. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1253554.

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Zio, Enrico, and Nicola Pedroni. Literature review of methods for representing uncertainty. Fondation pour une culture de sécurité industrielle, 2013. http://dx.doi.org/10.57071/124ure.

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This document provides a critical review of different frameworks for uncertainty analysis, in a risk analysis context: classical probabilistic analysis, imprecise probability (interval analysis), probability bound analysis, evidence theory, and possibility theory. The driver of the critical analysis is the decision-making process and the need to feed it with representative information derived from the risk assessment, to robustly support the decision. Technical details of the different frameworks are exposed only to the extent necessary to analyze and judge how these contribute to the communic
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