A Note on Learning Dependence Under Severe Uncertainty

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

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, December). A Note on Learning Dependence Under Severe Uncertainty. Presented at Information Processing and Management of Uncertainty in Knowledge-Based Systems, Montpellier, France

• Presentation Conference Type: Conference Paper (published)
• Conference Name: Information Processing and Management of Uncertainty in Knowledge-Based Systems
• 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.
• Public URL: https://durham-repository.worktribe.com/output/1154263
• Additional Information: 15th International Conference, IPMU 2014, Montpellier, France, July 15-19, 2014.