Robust Bayesian regression model in Bernstein form

In this paper, we present an inductive method for constructing robust Bayesian Polynomial Regression (BPR) models in Bernstein form, referred to as PRIAM (Polynomial Regression Inductive AlgorithM). PRIAM is an algorithm designed to determine stochastic dependence between variables. The triple nature of PRIAM combines the advantages of Bayesian inference, the interpretability of neurofuzzy models in Bernstein form, and the robustness of the support vector approach. This combination facilitates the integration of state-of-the-art machine learning techniques in decision support systems. We conduct experiments using well-known datasets and real-world economic, ecological, and meteorological models. Furthermore, we compare the forecast errors of PRIAM against several competitive algorithms.