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A note on learning dependence under severe uncertainty.

Troffaes, Matthias C. M. and Coolen, Frank P. A. and Destercke, Sebastien (2014) 'A note on learning dependence under severe uncertainty.', in Information processing and management of uncertainty in knowledge-based systems : 15th International Conference, IPMU 2014, Montpellier, France, July 15-19, 2014 ; proceedings, part III. , pp. 498-507. Communications in computer and information science. (444).


We propose two models, one continuous and one categorical, to learn about dependence between two random variables, given only limited joint observations, but assuming that the marginals are precisely known. The continuous model focuses on the Gaussian case, while the categorical model is generic. We illustrate the resulting statistical inferences on a simple example concerning the body mass index. Both methods can be extended easily to three or more random variables.

Item Type:Book chapter
Additional Information:15th International Conference, IPMU 2014, Montpellier, France, July 15-19, 2014.
Keywords:Bivariate data, Categorical data, Copula, Gaussian copula, Robust Bayesian, Imprecise probability.
Full text:Publisher-imposed embargo
(AM) Accepted Manuscript
File format - PDF (Copyright agreement prohibits open access to the full-text)
Publisher Web site:
Date accepted:No date available
Date deposited:17 October 2014
Date of first online publication:2014
Date first made open access:No date available

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