Marginalization using the metric of likelihood

Details Marginalization using the metric of likelihood Marginalization using the metric of likelihood

May 2001

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2001aipc..568..264p&link_type=abstract

BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 20th International Workshop. AIP Conference Proceedi

Computer Science

Information Theory

Measurement And Error Theory, Data Analysis: Algorithms And Implementation, Data Management, Information Theory And Communication Theory

Scientific paper

Although the likelihood function is normalizeable with respect to the data there is no guarantee that the same holds with respect to the model parameters. This may lead to singularities in the expectation value integral of these parameters, especially if the prior information is not sufficient to take care of finite integral values. However, the problem may be solved by obeying the correct Riemannian metric imposed by the likelihood. This will be demonstrated for the example of the electron temperature evaluation in hydrogen plasmas. .

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