Abstract
A nonlinear regression model on the basis of the covariance approximation of a multidimensional probability distribution is constructed. The model is represented by an expansion in the basis functions in the form of partial derivatives of the logarithm of the joint factor probability distribution. The weight coefficients of the expansion are the covariances of the resulting and explanatory variables. On particular examples, the efficiency of the Bayesian approximation of the proposed regression model in which the factor distribution is described by a finite mixture of ellipsoidally symmetric densities is demonstrated.
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