Bayesian training of mixture density networks

Lars U. Hjorth, Ian T. Nabney

Research output: Chapter in Book/Report/Conference proceedingChapter in a book

3 Citations (Scopus)


Mixture Density Networks (MDNs) are a well-established method for modelling the conditional probability density which is useful for complex multi-valued functions where regression methods (such as MLPs) fail. In this paper we extend earlier research of a regularisation method for a special case of MDNs to the general case using evidence based regularisation and we show how the Hessian of the MDN error function can be evaluated using R-propagation. The method is tested on two data sets and compared with early stopping.
Original languageUndefined/Unknown
Title of host publicationProceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000. IJCNN 2000
Place of PublicationUnited States
PublisherIEEE Computer Society
Number of pages6
ISBN (Print)9780769506197
Publication statusPublished - 1 Aug 2000


  • Bayes methods, learning, artificial intelligence, neural nets, Bayesian training, MDN error function, mixture density networks, R-propagation, conditional probability density

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