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Jeffrey's prior, a subset of Bayesian Statistics, is an objective prior distribution for a parameter space. A prior distribution is the probability distribution | Jeffrey's prior, a subset of Bayesian Statistics, is an objective prior distribution for a parameter space. A prior distribution is the probability distribution | ||
that expresses ones belief about the distribution before some evidence is taken into account. The density function of Jeffrey's prior is proportional to the square root of the determinant of the Fisher's Information matrix. This can be shown as: <math> p({\vec \theta }) \propto \sqrt{detI({\vec \theta })} </math> | that expresses ones belief about the distribution before some evidence is taken into account. The density function of Jeffrey's prior is proportional to the square root of the determinant of the Fisher's Information matrix. This can be shown as: <math> p({\vec \theta }) \propto \sqrt{detI({\vec \theta })} </math> | ||
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| + | One feature of this prior is that it is invariant for a change of coordinates of the vector {\vec \theta}. This means that | ||
Revision as of 20:35, 6 December 2020
Jeffrey's Prior
What is Jeffrey's Prior?
Jeffrey's prior, a subset of Bayesian Statistics, is an objective prior distribution for a parameter space. A prior distribution is the probability distribution that expresses ones belief about the distribution before some evidence is taken into account. The density function of Jeffrey's prior is proportional to the square root of the determinant of the Fisher's Information matrix. This can be shown as: $ p({\vec \theta }) \propto \sqrt{detI({\vec \theta })} $
One feature of this prior is that it is invariant for a change of coordinates of the vector {\vec \theta}. This means that
