Titlebook: Neural Networks for Conditional Probability Estimation; Forecasting Beyond P Dirk Husmeier Book 1999 Springer-Verlag London Limited 1999 al

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A Universal Approximator Network for Predicting Conditional Probability Densities,itecture can deal with both stochastic and determinstic processes. Two variants, the derivative-of-sigmoid mixture (DSM) and the Gaussian mixture (GM) networks are presented, and their relation to a stochastic kernel expansion is noted. The chapter concludes with a comparison between these models an

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