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The Failure-tree Analysis Based on Imprecise Probability and its Application on Tunnel Project
Chemical Engineering Transactions, 2017 Due to the inherent uncertainties in ground and groundwater conditions, tunnel projects often have to face potential risks of cost overrun or schedule delay.
Lei Huang
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, 2019 This chapter explores the topic of imprecise probabilities (IP) as it relates to model validation. IP is a family of formal methods that aim to provide a better representation of severe uncertainty than is possible with standard probabilistic methods. Among the methods discussed here are using sets of probabilities to represent uncertainty, and using ...
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Stable Non-standard Imprecise Probabilities [PDF]
, 2014 Stability arises as the consistency criterion in a betting interpretation for hyperreal imprecise previsions, that is imprecise previsions (and probabilities) which may take infinitesimal values. The purpose of this work is to extend the notion of stable coherence introduced in [8] to conditional hyperreal imprecise probabilities.
F. Montagna, H. Hosni
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Probabilistic satisfiability with imprecise probabilities
International Journal of Approximate Reasoning, 2000 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hansen, Pierre +4 more
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Hitting Times and Probabilities for Imprecise Markov Chains [PDF]
, 2019 We consider the problem of characterising expected hitting times and hitting probabilities for imprecise Markov chains. To this end, we consider three distinct ways in which imprecise Markov chains have been defined in the literature: as sets of ...
De Bock, Jasper +2 more
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Bayesian Learning for a Class of Priors with Prescribed Marginals [PDF]
, 2006 We present Bayesian updating of an imprecise probability measure, represented by a class of precise multidimensional probability measures. Choice and analysis of our class are motivated by expert interviews that we conducted with modelers in the context ...
Augustin, Thomas +2 more
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Combining Binary Classifiers with Imprecise Probabilities [PDF]
, 2011 This paper proposes a simple framework to combine binary classifiers whose outputs are imprecise probabilities (or are transformed into some imprecise probabilities, e.g., by using confidence intervals). This combination comes down to solve linear programs describing constraints over events (here, subsets of classes).
Destercke, Sébastien, Quost, Benjamin
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Modeling rational decisions in ambiguous situations: a multi-valued logic approach
Business Research, 2019 If a decision context is completely precise, making good decisions is relatively easy. In the presence of ambiguity, rational decision-making is incomparably more challenging.
Olga Metzger, Thomas Spengler
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Criticality assessment risk; contribution of fuzzy logic [PDF]
Yugoslav Journal of Operations Research, 2018 In order to determine the criticality of a risk, an assessment of the probability of occurrence (notion of frequency) and of the impact (notion of severity) are to be estimated.
Masmoudi S., Dhiaf M.M.
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Believing Probabilistic Contents: On the Expressive Power and Coherence of Sets of Sets of Probabilities [PDF]
, 2019 Moss (2018) argues that rational agents are best thought of not as having degrees of belief in various propositions but as having beliefs in probabilistic contents, or probabilistic beliefs.
Campbell-Moore, Catrin, Konek, Jason
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