Timeline for MLE vs MAP estimation, when to use which?
Current License: CC BY-SA 3.0
10 events
| when toggle format | what | by | license | comment | |
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| Dec 7, 2018 at 17:02 | comment | added | Heisenbug | @MichaelChernick I completely agree on your point of parameters being a point estimate in MLE and a distribution in MAP. Going from here, what will be the answer to the originally asked question. When will I use MAP? In other words, how does one know if the prior(if available) is good or not? | |
| Mar 18, 2018 at 17:53 | comment | added | Michael R. Chernick | If you have a lot of good information to construct a prior the MAP could be better when the sample size is small. These comparisons are only valid if you treat MLE and MAP as point estimates of the parameter. That would not be the viewpoint of a Bayesian, | |
| Mar 18, 2018 at 17:49 | comment | added | Michael R. Chernick | It depends on the prior and the amount of data. They can give similar results in large samples. The difference is in the interpretation. My comment was meant to show that it is not as simple as you make it. With a small amount of data it is not simply a matter of picking MAP if you have a prior. A poorly chosen prior can lead to getting a poor posterior distribution and hence a poor MAP. | |
| Mar 18, 2018 at 17:14 | comment | added | Heisenbug | @MichaelChernick - Thank you for your input. But doesn't MAP behave like an MLE once we have suffcient data. If we break the MAP expression we get an MLE term also. With large amount of data the MLE term in the MAP takes over the prior. | |
| Mar 17, 2018 at 23:43 | comment | added | Michael R. Chernick | The frequentist approach and the Bayesian approach are philosophically different. The frequency approach estimates the value of model parameters based on repeated sampling. The Bayesian approach treats the parameter as a random variable. So in the Bayesian approach you derive the posterior distribution of the parameter combining a prior distribution with the data. MAP looks for the highest peak of the posterior distribution while MLE estimates the parameter by only looking at the likelihood function of the data. | |
| Mar 17, 2018 at 23:22 | comment | added | Heisenbug | @MichaelChernick I might be wrong. I read this in grad school. I request that you correct me where i went wrong. | |
| Mar 17, 2018 at 18:16 | comment | added | Michael R. Chernick | It isn't that simple. | |
| Mar 17, 2018 at 17:32 | review | Late answers | |||
| Mar 17, 2018 at 18:16 | |||||
| Mar 17, 2018 at 17:17 | review | First posts | |||
| Mar 17, 2018 at 19:45 | |||||
| Mar 17, 2018 at 17:13 | history | answered | Heisenbug | CC BY-SA 3.0 |