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A Note on Learning Dependence Under Severe Uncertainty

Troffaes, Matthias C.M.; Coolen, Frank P.A.; Destercke, Sebastien

Authors

Frank P.A. Coolen

Sebastien Destercke



Abstract

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.

Citation

Troffaes, M. C., Coolen, F. P., & Destercke, S. (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 (498-507). https://doi.org/10.1007/978-3-319-08852-5_51

Conference Name Information Processing and Management of Uncertainty in Knowledge-Based Systems
Conference Location Montpellier, France
Publication Date Jul 19, 2014
Deposit Date Oct 15, 2014
Pages 498-507
Series Title Communications in computer and information science
Series Number 444
Series ISSN 1865-0929
Book Title Information processing and management of uncertainty in knowledge-based systems : 15th International Conference, IPMU 2014, Montpellier, France, July 15-19, 2014 ; proceedings, part III.
ISBN 9783319088518
DOI https://doi.org/10.1007/978-3-319-08852-5_51
Keywords Bivariate data, Categorical data, Copula, Gaussian copula, Robust Bayesian, Imprecise probability.
Additional Information 15th International Conference, IPMU 2014, Montpellier, France, July 15-19, 2014.

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