Results 271 to 280 of about 1,624,324 (312)
Perspectives on the quality of climate information for adaptation decision support. [PDF]
Clim ChangeBaldissera Pacchetti M +4 more
europepmc +1 more source
Interpreting imprecise probabilities
The Philosophical QuarterlyIt is essential that formal models come with interpretations: accounts of how the models relate to the phenomena. The traditional representation of degrees of belief as mathematical probabilities comes with a clear and simple interpretative story. This
Nicholas J.J. Smith
semanticscholar +2 more sources
Transitive Reasoning with Imprecise Probabilities [PDF]
European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty, 2015 We study probabilistically informative (weak) versions of transitivity by using suitable definitions of defaults and negated defaults in the setting of coherence and imprecise probabilities.
A. Gilio +2 more
semanticscholar +5 more sources
Simulation Foundations, Methods and Applications, 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 ...
S. Bradley
semanticscholar +3 more sources
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Discounting Imprecise Probabilities
, 2018In this paper it is considered the problem of discounting a credal set of probability distributions by a factor \(\alpha \) representing a degree of unreliability of the information source providing the imprecise probabilistic information. An axiomatic approach is followed by giving a set of properties that this operator should satisfy.
S. Moral
semanticscholar +2 more sources
Probabilistic Opinion Pooling with Imprecise Probabilities [PDF]
Journal of Philosophical Logic, 2018 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Rush T. Stewart, I. Quintana
semanticscholar +4 more sources
Imprecise Probabilities Meet Partial Observability: Game Semantics for Robust POMDPs
International Joint Conference on Artificial IntelligencePartially observable Markov decision processes (POMDPs) rely on the key assumption that probability distributions are precisely known. Robust POMDPs (RPOMDPs) alleviate this concern by defining imprecise probabilities, referred to as uncertainty sets ...
Eline M. Bovy +3 more
semanticscholar +1 more source
Scoring Rules and Calibration for Imprecise Probabilities
arXiv.orgWhat does it mean to say that, for example, the probability for rain tomorrow is between 20% and 30%? The theory for the evaluation of precise probabilistic forecasts is well-developed and is grounded in the key concepts of proper scoring rules and ...
Christian Fröhlich +1 more
semanticscholar +1 more source
Event‐Tree Analysis with Imprecise Probabilities
Risk Analysis, 2011Novel methods are proposed for dealing with event‐tree analysis under imprecise probabilities, where one could measure chance or uncertainty without sharp numerical probabilities and express available evidence as upper and lower previsions (or expectations) of gambles (or bounded real functions). Sets of upper and lower previsions generate a convex set
You, Xiaomin, Tonon, Fulvio
openaire +3 more sources
Introduction to Imprecise Probabilities
2020Since uncertainty is persistent in engineering analyses, this chapter aimed to introduce methods to describe and reason with under uncertainty in various scenarios. Probability theory is the most widely used methodology for uncertainty quantification for a long time and has proven to be a powerful tool for this task.
Daniel Krpelík, Tathagata Basu
openaire +1 more source