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Bayes estimates as an approximation to maximum likelihood estimates
Author(s) -
Yamamura Kohji
Publication year - 2016
Publication title -
population ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.819
H-Index - 59
eISSN - 1438-390X
pISSN - 1438-3896
DOI - 10.1007/s10144-015-0526-x
Subject(s) - bayes' theorem , statistics , bayes factor , prior probability , bayes estimator , mathematics , estimation , transformation (genetics) , maximum a posteriori estimation , maximum likelihood , scale parameter , bayes' rule , scale (ratio) , distribution (mathematics) , econometrics , bayesian probability , biology , economics , mathematical analysis , physics , biochemistry , management , quantum mechanics , gene
Ronald A. Fisher, who is the founder of maximum likelihood estimation (ML estimation), criticized the Bayes estimation of using a uniform prior distribution, because we can create estimates arbitrarily if we use Bayes estimation by changing the transformation used before the analysis. Thus, the Bayes estimates lack the scientific objectivity, especially when the amount of data is small. However, we can use the Bayes estimates as an approximation to the objective ML estimates if we use an appropriate transformation that makes the posterior distribution close to a normal distribution. One‐to‐one correspondence exists between a uniform prior distribution under a transformed scale and a non‐uniform prior distribution under the original scale. For this reason, the Bayes estimation of ML estimates is essentially identical to the estimation using Jeffreys prior.