Letteratura scientifica selezionata sul tema "Skeptical inference"
Cita una fonte nei formati APA, MLA, Chicago, Harvard e in molti altri stili
Consulta la lista di attuali articoli, libri, tesi, atti di convegni e altre fonti scientifiche attinenti al tema "Skeptical inference".
Accanto a ogni fonte nell'elenco di riferimenti c'è un pulsante "Aggiungi alla bibliografia". Premilo e genereremo automaticamente la citazione bibliografica dell'opera scelta nello stile citazionale di cui hai bisogno: APA, MLA, Harvard, Chicago, Vancouver ecc.
Puoi anche scaricare il testo completo della pubblicazione scientifica nel formato .pdf e leggere online l'abstract (il sommario) dell'opera se è presente nei metadati.
Articoli di riviste sul tema "Skeptical inference"
Walker, Mark. "Occam’s Razor, Dogmatism, Skepticism, and Skeptical Dogmatism". International Journal for the Study of Skepticism 6, n. 1 (15 marzo 2016): 1–29. http://dx.doi.org/10.1163/22105700-05011168.
Testo completoHENDRICKS, PERRY. "Skeptical Theism Proved". Journal of the American Philosophical Association 6, n. 2 (2020): 264–74. http://dx.doi.org/10.1017/apa.2019.45.
Testo completoDe Cooman, Gert, Jasper De Bock e Márcio Alves Diniz. "Coherent Predictive Inference under Exchangeability with Imprecise Probabilities". Journal of Artificial Intelligence Research 52 (10 gennaio 2015): 1–95. http://dx.doi.org/10.1613/jair.4490.
Testo completoFirebaugh, Glenn. "Will Bayesian Inference Help? A Skeptical View". Sociological Methodology 25 (1995): 469. http://dx.doi.org/10.2307/271075.
Testo completoBeierle, Christoph, Christian Eichhorn, Gabriele Kern-Isberner e Steven Kutsch. "Properties and interrelationships of skeptical, weakly skeptical, and credulous inference induced by classes of minimal models". Artificial Intelligence 297 (agosto 2021): 103489. http://dx.doi.org/10.1016/j.artint.2021.103489.
Testo completoMills, Ethan. "Three Skepticisms in Cārvāka Epistemology: The Problem of Induction, Purandara’s Fallibilism, and Jayarāśi’s Skepticism about Philosophy". International Journal for the Study of Skepticism 12, n. 1 (23 dicembre 2021): 46–71. http://dx.doi.org/10.1163/22105700-bja10029.
Testo completoBeierle, Christoph, Christian Eichhorn, Gabriele Kern-Isberner e Steven Kutsch. "Properties of skeptical c-inference for conditional knowledge bases and its realization as a constraint satisfaction problem". Annals of Mathematics and Artificial Intelligence 83, n. 3-4 (1 febbraio 2018): 247–75. http://dx.doi.org/10.1007/s10472-017-9571-9.
Testo completoKomo, Christian, e Christoph Beierle. "Nonmonotonic reasoning from conditional knowledge bases with system W". Annals of Mathematics and Artificial Intelligence 90, n. 1 (14 dicembre 2021): 107–44. http://dx.doi.org/10.1007/s10472-021-09777-9.
Testo completoBlackwell, Matthew. "A Selection Bias Approach to Sensitivity Analysis for Causal Effects". Political Analysis 22, n. 2 (2014): 169–82. http://dx.doi.org/10.1093/pan/mpt006.
Testo completoMaddox, Bryan. "On the Motivations of a Skeptic, and Her Practice". Peitho. Examina Antiqua 7, n. 1 (17 marzo 2016): 229–48. http://dx.doi.org/10.14746/pea.2016.1.12.
Testo completoTesi sul tema "Skeptical inference"
Carranza, Alarcón Yonatan Carlos. "Distributionally robust, skeptical inferences in supervised classification using imprecise probabilities". Thesis, Compiègne, 2020. http://www.theses.fr/2020COMP2567.
Testo completoDecision makers are often faced with making single hard decisions, without having any knowledge of the amount of uncertainties contained in them, and taking the risk of making damaging, if not dramatic, mistakes. In such situations, where the uncertainty is higher due to imperfect information, it may be useful to provide set-valued but more reliable decisions. This works thus focuses on making distributionally robust, skeptical inferences (or decisions) in supervised classification problems using imprecise probabilities. By distributionally robust, we mean that we consider a set of possible probability distributions, i.e. imprecise probabilities, and by skeptical we understand that we consider as valid only those inferences that are true for every distribution within this set. Specifically, we focus on extending the Gaussian discriminant analysis and multilabel classification approaches to the imprecise probabilistic setting. Regarding to Gaussian discriminant analysis, we extend it by proposing a new imprecise classifier, considering the imprecision as part of its basic axioms, based on robust Bayesian analysis and near-ignorance priors. By including an imprecise component in the model, our proposal highlights those hard instances on which the precise model makes mistakes in order to provide cautious decisions in the form of set-valued class, instead. Regarding to multi-label classification, we first focus on reducing the time complexity of making a cautious decision over its output space of exponential size by providing theoretical justifications and efficient algorithms applied to the Hamming loss. Relaxing the assumption of independence on labels, we obtain partial decisions, i.e. not classifying at all over some labels, which generalize the binary relevance approach by using imprecise marginal distributions. Secondly, we extend the classifierchains approach by proposing two different strategies to handle imprecise probabilityestimates, and a new dynamic, context-dependent label ordering which dynamically selects the labels with low uncertainty as the chain moves forwards
Willot, Hénoïk. "Certified explanations of robust models". Electronic Thesis or Diss., Compiègne, 2024. http://www.theses.fr/2024COMP2812.
Testo completoWith the advent of automated or semi-automated decision systems in artificial intelligence comes the need of making them more reliable and transparent for an end-user. While the role of explainable methods is in general to increase transparency, reliability can be achieved by providing certified explanations, in the sense that those are guaranteed to be true, and by considering robust models that can abstain when having insufficient information, rather than enforcing precision for the mere sake of avoiding indecision. This last aspect is commonly referred to as skeptical inference. This work participates to this effort, by considering two cases: - The first one considers classical decision rules used to enforce fairness, which are the Ordered Weighted Averaging (OWA) with decreasing weights. Our main contribution is to fully characterise from an axiomatic perspective convex sets of such rules, and to provide together with this sound and complete explanation schemes that can be efficiently obtained through heuristics. Doing so, we also provide a unifying framework between the restricted and generalized Lorenz dominance, two qualitative criteria, and precise decreasing OWA. - The second one considers that our decision rule is a classification model resulting from a learning procedure, where the resulting model is a set of probabilities. We study and discuss the problem of providing prime implicant as explanations in such a case, where in addition to explaining clear preferences of one class over the other, we also have to treat the problem of declaring two classes as being incomparable. We describe the corresponding problems in general ways, before studying in more details the robust counter-part of the Naive Bayes Classifier
Libri sul tema "Skeptical inference"
McCain, Kevin, e Ted Poston, a cura di. Best Explanations. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198746904.001.0001.
Testo completoBeebe, James R. Does Skepticism Presuppose Explanationism? Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198746904.003.0011.
Testo completoStegenga, Jacob. Introduction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198747048.003.0001.
Testo completoMillican, Peter. Hume’s Chief Argument. A cura di Paul Russell. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199742844.013.32.
Testo completoRinard, Susanna. External World Skepticism and Inference to the Best Explanation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198746904.003.0013.
Testo completoJohnsen, Bredo. David Hume. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190662776.003.0005.
Testo completoKornblith, Hilary. Scientific Epistemology. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780197609552.001.0001.
Testo completoBaggett, David. Moral Arguments (actually R1 to Rn). Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190842215.003.0016.
Testo completoCapitoli di libri sul tema "Skeptical inference"
Bochman, Alexander. "Skeptical Inference Relations". In A Logical Theory of Nonmonotonic Inference and Belief Change, 163–212. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04560-2_7.
Testo completoVahid, Hamid. "Argument from Inference to the Best Explanation (IBE)". In Epistemic Justification and the Skeptical Challenge, 181–98. London: Palgrave Macmillan UK, 2005. http://dx.doi.org/10.1057/9780230596214_10.
Testo completoBeierle, Christoph, e Steven Kutsch. "Regular and Sufficient Bounds of Finite Domain Constraints for Skeptical C-Inference". In Advances in Artificial Intelligence: From Theory to Practice, 477–87. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60042-0_52.
Testo completoBeierle, Christoph, Christian Eichhorn e Gabriele Kern-Isberner. "Skeptical Inference Based on C-Representations and Its Characterization as a Constraint Satisfaction Problem". In Lecture Notes in Computer Science, 65–82. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30024-5_4.
Testo completoSkyrms, Brian. "Evolution of Inference". In Dynamics in Human and Primate Societies. Oxford University Press, 2000. http://dx.doi.org/10.1093/oso/9780195131673.003.0009.
Testo completoAli, Arden. "Manifestations of Virtue". In Oxford Studies in Normative Ethics Volume 10, 229–54. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198867944.003.0011.
Testo completoDe Pierris, Graciela. "Hume’s Skeptical Treatment of the Causal Inductive Inference". In Ideas, Evidence, and Method, 197–258. Oxford University Press, 2015. http://dx.doi.org/10.1093/acprof:oso/9780198716785.003.0005.
Testo completoBergmann, Michael. "Inferential Anti-skepticism about Perception". In Radical Skepticism and Epistemic Intuition, 35–56. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192898487.003.0003.
Testo completoBrown, James L. D. "Conceptual Role Expressivism and Defective Concepts". In Oxford Studies in Metaethics, Volume 17, 225–53. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192865601.003.0009.
Testo completoBergmann, Michael. "Underdetermination and Perceptual Skepticism". In Radical Skepticism and Epistemic Intuition, 15–34. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192898487.003.0002.
Testo completoAtti di convegni sul tema "Skeptical inference"
Rudinger, Rachel, Vered Shwartz, Jena D. Hwang, Chandra Bhagavatula, Maxwell Forbes, Ronan Le Bras, Noah A. Smith e Yejin Choi. "Thinking Like a Skeptic: Defeasible Inference in Natural Language". In Findings of the Association for Computational Linguistics: EMNLP 2020. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.findings-emnlp.418.
Testo completo