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Proceedings Paper

Exploring the reliability of Bayesian reconstructions
Author(s): Kenneth M. Hanson; Gregory S. Cunningham
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Paper Abstract

The Bayesian approach allows one to combine measurement data with prior knowledge about models of reality to draw inferences about the validity of those models. The posterior probability quantifies the degree of certainty one has about those models. We propose a method to explore the reliability, or uncertainty, of specific features of a Bayesian solution. If on draws an analogy between the negative logarithm of the posterior and a physical potential, the gradient of this potential can be interpreted as a force that acts on the model. As model parameters are perturbed from their maximum a posteriori (MAP) values, the strength of the restoring force that drives them back to the MAP solution is directly related to the uncertainty in those parameter estimates. The correlations between the uncertainties of parameter estimates can be elucidated.

Paper Details

Date Published: 12 May 1995
PDF: 8 pages
Proc. SPIE 2434, Medical Imaging 1995: Image Processing, (12 May 1995); doi: 10.1117/12.208713
Show Author Affiliations
Kenneth M. Hanson, Los Alamos National Lab. (United States)
Gregory S. Cunningham, Los Alamos National Lab. (United States)


Published in SPIE Proceedings Vol. 2434:
Medical Imaging 1995: Image Processing
Murray H. Loew, Editor(s)

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