Bayesian regression filter and the issue of priors

Zhu, Huaiyu and Rohwer, Richard (1996). Bayesian regression filter and the issue of priors. Neural Computing and Applications, 4 (3), pp. 130-142.

Abstract

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.

Publication DOI: https://doi.org/10.1007/BF01414873
Divisions: Aston University (General)
Additional Information: The original publication is available at www.springerlink.com
Uncontrolled Keywords: Bayesian framework,regression problems,Kalman filter,Simulations
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Related URLs: http://www.spri ... 1hr600k7023276/ (Publisher URL)
Published Date: 1996-09
Authors: Zhu, Huaiyu
Rohwer, Richard

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Version: Accepted Version


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