Resumen

BOADA, Antonio José. BAYESIAN DYNAMIC LINEAR MODEL AS AN AUTOMATIC UPDATE PROCEDURE FOR PREDICTIVE STATISTICAL MODELS. Dimens.empres. [online]. 2017, vol.15, n.1, pp.30-49. ISSN 1692-8563.  https://doi.org/10.15665/rde.v15i1.547.

This paper, a practical application, proven through actual data, how the Bayesian Dynamic Linear Model Order 1 can be applied directly to the random waste from a multiple regression model Classic Static, thus creating an interesting addition is exposed for predictive statistical models. This Bayesian component generates a retro factor that feeds on waste (difference between predictions and actual historical values), adjusted according to the most recent historical information, all of them automatically and without the need to continually adjusts the Multiple Regression coefficients, generating an increase in the strength and stability of such models for prediction automated tools companies. This article provides a case of how Bayesian statistics can be an excellent complement to the techniques of classical frequentist statistics.

Palabras clave : Bayesian Model; Waste Analysis; Bayesian prediction; Automated Estimation.

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