A Note on Learning Dependence Under Severe Uncertainty

Matthias M.C.M Troffaes (1) , Frank Coolen (1) , Sébastien Destercke (2, 3)

1 Department of Mathematical Sciences
2 Labex MS2T - Laboratoire d'Excellence "Maîtrise des Systèmes de Systèmes Technologiques"
3 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]

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.

Domaines: Méthodologie [stat.ME]

Matthias M.C.M Troffaes, Frank Coolen, Sébastien Destercke. A Note on Learning Dependence Under Severe Uncertainty. International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2014), Jul 2014, Montpellier, France. pp.498-507. ⟨hal-01045006⟩