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Littérature scientifique sur le sujet « Inferenza cauta »
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Articles de revues sur le sujet "Inferenza cauta"
Rodríguez-Villamizar, Laura Andrea. « Inferencia causal en epidemiología ». Revista de Salud Pública 19, no 3 (1 mai 2017) : 409–15. http://dx.doi.org/10.15446/rsap.v19n3.66180.
Texte intégralSalvini, Alessandro, et Antonio Iudici. « Le attribuzioni di causa e il giudizio clinico in psichiatria forense e psicologia giuridica ». RIVISTA SPERIMENTALE DI FRENIATRIA, no 2 (juillet 2011) : 69–81. http://dx.doi.org/10.3280/rsf2011-002006.
Texte intégralGamboa, Lydia Deni. « La teoría de Adam of Wodeham sobre la percepción no verídica de círculos suspendidos en el aire ». Perseitas 8 (10 juillet 2020) : 295. http://dx.doi.org/10.21501/23461780.3669.
Texte intégralOcampo-Duque, William, Carolina Osorio, Christian Piamba, Marta Schuhmacher et José L. Domingo. « Water quality analysis in rivers with non-parametric probability distributions and fuzzy inference systems : Application to the Cauca River, Colombia ». Environment International 52 (février 2013) : 17–28. http://dx.doi.org/10.1016/j.envint.2012.11.007.
Texte intégralMatute Clavier, Arnaldo, et William Fernando Bernal Suárez. « Técnicas de lógica difusa en ingeniería de control ». Revista Ciencia, Innovación y Tecnología 3 (27 novembre 2017) : 125–34. http://dx.doi.org/10.38017/2390058x.81.
Texte intégralFranco Silva, Talita Cristina Marques, et João Fernando Marcolan. « Preconceito aos indivíduos com transtorno mental como agravo do sofrimento ». Revista de Enfermagem UFPE on line 12, no 8 (4 août 2018) : 2089. http://dx.doi.org/10.5205/1981-8963-v12i8a234776p2089-2098-2018.
Texte intégralPicón, Mariantonella. « La causalidad de la psicopatía : rasgos y características ». Revista de Investigación Científica y Tecnológica 5, no 1 (30 juin 2021) : 84–89. http://dx.doi.org/10.36003/rev.investig.cient.tecnol.v5n1(2021)7.
Texte intégralQuirama, Uvenny, Paula Forero, Diego Montañez, Diana Mena et Henner Solarte. « Resolviendo endogeneidad sin instrumentos. Una aplicación desde Lewbel ». Unaciencia Revista de Estudios e Investigaciones 13, no 24 (15 juillet 2020) : 71–83. http://dx.doi.org/10.35997/runacv13n24a9.
Texte intégralElleuch, Hanene, et Ali Wali. « UNWEARABLE MULTI-MODAL GESTURES RECOGNITION SYSTEM FOR INTERACTION WITH MOBILE DEVICES IN UNEXPECTED SITUATIONS ». IIUM Engineering Journal 20, no 2 (2 décembre 2019) : 142–62. http://dx.doi.org/10.31436/iiumej.v20i2.1000.
Texte intégralHiken, Jeffrey, Richard LeDuc, Petra Gilmore, Henry Rohrs, R. Reid Townsend et Monica Bessler. « Global Differences in RBC Membrane Protein Expression Between Normal and PNH Individuals. » Blood 114, no 22 (20 novembre 2009) : 1986. http://dx.doi.org/10.1182/blood.v114.22.1986.1986.
Texte intégralThèses sur le sujet "Inferenza cauta"
CAMPAGNER, ANDREA. « Robust Learning Methods for Imprecise Data and Cautious Inference ». Doctoral thesis, Università degli Studi di Milano-Bicocca, 2023. https://hdl.handle.net/10281/404829.
Texte intégralThe representation, quantification and proper management of uncertainty is one of the central problems in Artificial Intelligence, and particularly so in Machine Learning, in which uncertainty is intrinsically tied to the inductive nature of the learning problem. Among different forms of uncertainty, the modeling of imprecision, that is the problem of dealing with data or knowledge that are imperfect} and incomplete, has recently attracted interest in the research community, for its theoretical and application-oriented implications on the practice and use of Machine Learning-based tools and methods. This work focuses on the problem of dealing with imprecision in Machine Learning, from two different perspectives. On the one hand, when imprecision affects the input data to a Machine Learning pipeline, leading to the problem of learning from imprecise data. On the other hand, when imprecision is used a way to implement uncertainty quantification for Machine Learning methods, by allowing these latter to provide set-valued predictions, leading to so-called cautious inference methods. The aim of this work, then, will be to investigate theoretical as well as empirical issues related to the two above mentioned settings. Within the context of learning from imprecise data, focus will be given on the investigation of the learning from fuzzy labels setting, both from a learning-theoretical and algorithmic point of view. Main contributions in this sense include: a learning-theoretical characterization of the hardness of learning from fuzzy labels problem; the proposal of a novel, pseudo labels-based, ensemble learning algorithm along with its theoretical study and empirical analysis, by which it is shown to provide promising results in comparison with the state-of-the-art; the application of this latter algorithm in three relevant real-world medical problems, in which imprecision occurs, respectively, due to the presence of conflicting expert opinions, the use of vague technical vocabulary, and the presence of individual variability in biochemical parameters; as well as the proposal of feature selection algorithms that may help in reducing the computational complexity of this task or limit the curse of dimensionality. Within the context of cautious inference, focus will be given to the theoretical study of three popular cautious inference frameworks, as well as to the development of novel algorithms and approaches to further the application of cautious inference in relevant settings. Main contributions in this sense include the study of the theoretical properties of, and relationships among, decision-theoretic, selective prediction and conformal prediction methods; the proposal of novel cautious inference techniques drawing from the interaction between decision-theoretic and conformal predictions methods, and their evaluation in medical settings; as well as the study of ensemble of cautious inference models, both from an empirical point of view, as well as from a theoretical one, by which it is shown that such ensembles could be useful to improve robustness, generalization, as well as to facilitate application of cautious inference methods on multi-source and multi-modal data.
Tasca, Gustavo Henrique 1990. « Inferência bayesiana para distribuições de cauda longa ». [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/307583.
Texte intégralDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação Científica
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Resumo: Neste trabalho, estudamos métodos de inferência bayesiana para distribuições de cauda longa, que não envolvam o cálculo da função de verossimilhança. Inicialmente, apresentamos uma análise das propriedades de distribuições de cauda pesada e seus casos particulares, como as famílias de distribuições de cauda longa, subexponenciais e de variação regular. Apresentamos algumas estatísticas e seus comportamentos amostrais, a fim de desenvolvermos medidas de diagnóstico. Para obtenção de inferências a posteriori, discutimos o método ABC de mínima entropia e outros algoritmos para verificação e seleção de modelos, que não utilizam o cálculo da função de verossimilhança. Introduzimos um novo algoritmo para seleção de modelos baseado na distribuição preditiva a posteriori, cujos resultados são validados através de simulações e análises de dados reais relacionados à hidrologia
Abstract: In this work, we study Bayesian inference methods for long-tailed distributions that don't involve the evaluation of the likelihood function. Initially, we present an analysis of the properties of heavy-tailed distributions and particular cases, as long-tailed, subexponencial and regular variation families. Some statistics are presented and their sampling behavior studied, in order to develop diagnostic measures. For obtaining posterior inferences, we discuss the minimum entropy ABC and others likelihood-free algorithms, aiming model checking and model selection. We introduce a new model selection algorithm based on the posterior predictive distribution, the results of which are validated through simulations and real data related to river flow
Mestrado
Estatistica
Mestre em Estatística
Santos, Junior James Dean Oliveira dos. « Considerações sobre a relação entre distribuições de cauda pesada e conflitos de informação em inferencia bayesiana ». [s.n.], 2006. http://repositorio.unicamp.br/jspui/handle/REPOSIP/306673.
Texte intégralDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Cientifica
Made available in DSpace on 2018-08-08T04:30:52Z (GMT). No. of bitstreams: 1 SantosJunior_JamesDeanOliveirados_M.pdf: 1844173 bytes, checksum: 122644f8bc0dedaaa7d7633d9b25eb9c (MD5) Previous issue date: 2006
Resumo: Em inferência bayesiana lidamos com informações provenientes dos dados e com informações a priori. Eventualmente, um ou mais outliers podem causar um conflito entre as fontes de informação. Basica!llente, resolver um conflito entre as fontes de informações implica em encontrar um conjunto de restrições tais que uma das fontes domine, em certo sentido, as demais. Têm-se utilizado na literatura distribuições amplamente aceitas como sendo de cauda pesada para este fim. Neste trabalho, mostramos as relações existentes entre alguns resultados da teoria de conflitos e as distribuições de caudas pesadas. Também mostramos como podemos resolver conflitos no caso locação utilizando modelos subexponenciais e como utilizar a medida credence para resolver problemas no caso escala
Abstract: In bayesian inference we deal with information proceeding from the data and prior information. Eventually, one ar more outliers can cause a conflict between the sources information. Basically, to decide a conflict between the sources of information implies in finding a set of restrictions such that one of the sources dominates, in certain sense, the outher. Widely distributions have been used in literature as being of heavy tailed for this end. In this work, we show the relations between some results of the theory of conflicts and the heavy tailed distributions. Also we show how we can decide a conflicts in the location case using subexponential models and how to use the measure credence to decide problems in the scale case
Mestrado
Inferencia Bayesiana
Mestre em Estatística