|Sep20-12, 02:23 AM||#1|
bayesian method vs.maximum likelihood
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?
|Sep20-12, 04:17 AM||#2|
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?
|Sep20-12, 07:05 AM||#3|
|Similar Threads for: bayesian method vs.maximum likelihood|
|maximum likelihood||Set Theory, Logic, Probability, Statistics||6|
|Bayesian inference of Poisson likelihood and exponential prior.||Calculus & Beyond Homework||0|
|Statistics: Method of Moments/Maximum Likelihood Estimation||Calculus & Beyond Homework||7|
|Estimation of parameters using maximum likelihood method||Calculus & Beyond Homework||0|
|Method of moments/maximum likelihood||Set Theory, Logic, Probability, Statistics||0|