An upper bound on the Bayesian error bars for generalized linear regression

Qazaz, C, Williams, Christopher K. I. and Bishop, Christopher M. (1997). An upper bound on the Bayesian error bars for generalized linear regression. IN: Mathematics of neural networks. Ellacott, Stephen W.; Mason, John C. and Anderson, Iain J. (eds) Operations Research/Computer Science Interfaces Series . Kluwer.


In the Bayesian framework, predictions for a regression problem are expressed in terms of a distribution of output values. The mode of this distribution corresponds to the most probable output, while the uncertainty associated with the predictions can conveniently be expressed in terms of error bars. In this paper we consider the evaluation of error bars in the context of the class of generalized linear regression models. We provide insights into the dependence of the error bars on the location of the data points and we derive an upper bound on the true error bars in terms of the contributions from individual data points which are themselves easily evaluated.

Divisions: Aston University (General)
Additional Information: Awaiting for publisher permission EW 06/07/2009 (c in E)
Uncontrolled Keywords: Bayesian,distribution,predictions,error bars bars,linear regression
Published Date: 1997
Authors: Qazaz, C
Williams, Christopher K. I.
Bishop, Christopher M.


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