Abstract
A key difficulty in the use of Gibbs prior distributions in Bayesian image analysis is the intractability of the normalisation constant. One approach is to perform off-line simulations which allow a calibration of normalisation constant against prior parameter. In this paper the reverse-logistic regression approach to calibration will be examined for various Gibbs distributions and explicit parametric equations will be proposed. A simple method for combining separate calibrations will be illustrated and the relationship between normalisation constant and image size will be explored with an empirical approximation proposed.
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