English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

This paper presents a general and efficient framework for probabilisticinference and learning from arbitrary uncertain information. It exploits thecalculation properties of finite mixture models, conjugate families andfactorization. Both the joint probability density of the variables and thelikelihood function of the (objective or subjective) observation areapproximated by a special mixture model, in such a way that any desiredconditional distribution can be directly obtained without numericalintegration. We have developed an extended version of the expectationmaximization (EM) algorithm to estimate the parameters of mixture models fromuncertain training examples (indirect observations). As a consequence, anypiece of exact or uncertain information about both input and output values isconsistently handled in the inference and learning stages. This ability,extremely useful in certain situations, is not found in most alternativemethods. The proposed framework is formally justified from standardprobabilistic principles and illustrative examples are provided in the fieldsof nonparametric pattern classification, nonlinear regression and patterncompletion. Finally, experiments on a real application and comparative resultsover standard databases provide empirical evidence of the utility of the methodin a wide range of applications.

Probabilistic Inference from Arbitrary Uncertainty using Mixtures of Factorized Generalized Gaussians