bayesian method vs.maximum likelihood

Mark J.

Hi,
Wondering if there is any priorities one method has versus the other one and are there any specific cases where to use one vs.other?

regards

chiro

Hey Mark J.

I'm not exactly sure what you mean specifically. The MLE is part of a massive framework used in point and interval estimation for statistical inference, but the bayesian stuff is a framework dealing with generalizing probabilistic situations where parameters of distributions are not constant (which leads to all kinds of other results both probabilistically and statistically).

Do you have a specific example of Bayesian Probability or Inference that you are referring to?

For example if you are talking about inference, are you talking about estimating parameters with a specific posterior and prior? Specific posterior and general prior? General posteriors and priors?

D H

The priority of MAP vs ML depends largely on whether one is already of a Bayesianist or frequentist mindset. Maximum a posteriori and maximum likelihood have their own lingo, their own sets of a massive underlying frameworks, their own set of heuristics for overcoming weaknesses in the methods. I've seen a few papers that compare MAP vs ML. However, if you look at the publications of the authors of such a paper before reading it, you can form a pretty solid prior regarding which technique will come out on top.