Bayesian methods for neural networks

Christopher M. Bishop

    Research output: Preprint or Working paperTechnical report


    Bayesian techniques have been developed over many years in a range of different fields, but have only recently been applied to the problem of learning in neural networks. As well as providing a consistent framework for statistical pattern recognition, the Bayesian approach offers a number of practical advantages including a potential solution to the problem of over-fitting. This chapter aims to provide an introductory overview of the application of Bayesian methods to neural networks. It assumes the reader is familiar with standard feed-forward network models and how to train them using conventional techniques.
    Original languageEnglish
    Place of PublicationBirmingham
    PublisherAston University
    Number of pages18
    ISBN (Print)NCRG/95/009
    Publication statusPublished - 1995


    • Bayesian
    • neural networks
    • learning
    • pattern recognitio


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    • Bayesian methods for neural networks

      Bishop, C. M., 1995, Oxford Lectures on Neural Networks. Tarassenko, L., Rolls, E. & Sherrington, D. (eds.). Oxford: Oxford University Press

      Research output: Chapter in Book/Published conference outputChapter

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