Reconstruction based on flexible prior models

A new approach to Bayesian reconstruction is introduced in which the prior probability distribution is endowed with an inherent geometrical flexibility. This flexibility is achieved through a warping of the coordinate system of the prior distribution into that of the reconstruction. This warping allows various degrees of mismatch between the assumed prior distribution and the actual distribution corresponding to the available measurements. The extent of the mismatch is readily controlled through constraints placed on the warp parameters