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It might be some straight forward thing.. But I referred to some threads already over internet to understand what exact does it mean when we use terms "evidence maximization" "type II maximum likelihood" or "maximum marginal likelihood".

All are same, and work with the marginal likelihood. i.e we maximize the denominator in Bayes Theorem. How does it differ from MLE?

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    $\begingroup$ Perhaps you might have a look at the following wiki on empirical Bayes which is the same as type-II maximum likelihood, only the latter name is not listed in wiki: en.wikipedia.org/wiki/Empirical_Bayes_method. $\endgroup$ – microhaus Mar 20 at 13:10
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Empirical Bayes is a means of using the observed data to compute point estimates of the hyperparameters parametrising your priors. Which only makes sense in context of a hierarchical Bayesian model, where you have hyperparameters which parametrise priors on your model parameters.

Maximum likelihood is a frequentist approach - you compute point estimates of the parameters, and there is no uncertainty being modelled in these parameters through the use of priors, parametrised by hyperparameters, on said parameters.

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  • $\begingroup$ Can you say even further that any point estimate is frequentist approach? $\endgroup$ – Good Luck Mar 23 at 20:53

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