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Bayesian method vs.maximum likelihood

  1. Sep 20, 2012 #1
    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?

  2. jcsd
  3. Sep 20, 2012 #2


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    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?
  4. Sep 20, 2012 #3

    D H

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    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.
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