REPORT Moerland-97.5/IDIAP Mixtures of Experts Estimate A Posteriori Probabilities Moerland, Perry EXTERNAL https://publications.idiap.ch/attachments/reports/1997/rr97-07.pdf PUBLIC Idiap-RR-07-1997 1997 IDIAP Published in ``Proceedings of the International Conference on Artificial Neural Networks (ICANN'97)'' The mixtures of experts (ME) model offers a modular structure suitable for a divide-and-conquer approach to pattern recognition. It has a probabilistic interpretation in terms of a mixture model, which forms the basis for the error function associated with MEs. In this paper, it is shown that for classification problems the minimization of this ME error function leads to ME outputs estimating the a posteriori probabilities of class membership of the input vector.