The hard truth
In the Bayesian methodology, the posterior probability combines uncertainty about prior knowledge, and available data about alternative models of reality. The posterior quantifies the degree of certainty one has in inferring the truth in terms of those models. We propose a method to determine the reliability of a specific feature of a Bayesian solution. Our approach is based on an analogy between the negative logarithm of the posterior and a physical potential. This analogy leads to the interpretation of gradient of this potential 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 reliability of those parameter estimates. The correlations between the uncertainties of parameter estimates can be elucidated.
- Research Organization:
- Los Alamos National Lab., NM (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- W-7405-ENG-36
- OSTI ID:
- 10114095
- Report Number(s):
- LA-UR--94-4385; CONF-9408207--1; ON: DE95005240
- Country of Publication:
- United States
- Language:
- English
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