Bayesian regression filter and the issue of priors

Huaiyu Zhu, Richard Rohwer

    Research output: Contribution to journalArticlepeer-review


    We propose a Bayesian framework for regression problems, which covers areas which are usually dealt with by function approximation. An online learning algorithm is derived which solves regression problems with a Kalman filter. Its solution always improves with increasing model complexity, without the risk of over-fitting. In the infinite dimension limit it approaches the true Bayesian posterior. The issues of prior selection and over-fitting are also discussed, showing that some of the commonly held beliefs are misleading. The practical implementation is summarised. Simulations using 13 popular publicly available data sets are used to demonstrate the method and highlight important issues concerning the choice of priors.
    Original languageEnglish
    Pages (from-to)130-142
    Number of pages13
    JournalNeural Computing and Applications
    Issue number3
    Publication statusPublished - Sept 1996

    Bibliographical note

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    • Bayesian framework
    • regression problems
    • Kalman filter
    • Simulations


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