Evaluating probabilistic models learned from data

Several learning algorithms have been proposed to construct probabilistic models from data using the Bayesian networks mechanism. Some of them permit the participation of human experts in order to create a knowledge representation of the domain. However, multiple different models may result for the same problem using the same data set. This paper presents the experiences in the construction of a probabilistic model that conforms a viscosity virtual sensor. Several experiments have been conduced and several different models have been obtained. This paper describes the evaluation implemented of all models under different criteria. The analysis of the models and the conclusions identified are included in this paper.