Sciweavers

Share
JAIR
1998

Probabilistic Inference from Arbitrary Uncertainty using Mixtures of Factorized Generalized Gaussians

8 years 5 months ago
Probabilistic Inference from Arbitrary Uncertainty using Mixtures of Factorized Generalized Gaussians
This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of finite mixture models, conjugate families and factorization. Both the joint probability density of the variables and the likelihood function of the (objective or subjective) observation are approximated by a special mixture model, in such a way that any desired conditional distribution can be directly obtained without numerical integration. We have developed an extended version of the expectation maximization (EM) algorithm to estimate the parameters of mixture models from uncertain training examples (indirect observations). As a consequence, any piece of exact or uncertain information about both input and output values is consistently handled in the inference and learning stages. This ability, extremely useful in certain situations, is not found in most alternative methods. The proposed framework is formally jus...
Alberto Ruiz, Pedro E. López-de-Teruel, M.
Added 22 Dec 2010
Updated 22 Dec 2010
Type Journal
Year 1998
Where JAIR
Authors Alberto Ruiz, Pedro E. López-de-Teruel, M. Carmen Garrido
Comments (0)
books