Timeline for Estimation in Naive Bayes
Current License: CC BY-SA 3.0
3 events
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| Jul 1, 2014 at 9:11 | comment | added | rapaio | MAP in obtained by using a prior. In this case when the prior is uniform, this is equivalent with additive smoothing, or Laplace smoothing. So, saying that you do not need MAP because you use MLE with smoothing is wrong, because MLE with smoothing is a particular case of MAP. Also, MAP can be used also to incorporate other kind of priors. Also, you can use a mean estimator, which is a combination of prior mean and MLE. And, finally, both of them are not fully Bayesian since are point estimates. A full treatment would involve to integrate out the parameters. | |
| Jun 25, 2014 at 22:03 | comment | added | HIGGINS | Thank you. I was of this view. NLTK is implementing ELEProbdist sort of MLE, but Manning & Schutze recommended for MAP, though I did not find any reason for it. If additive smoothing has to be introduced then why MAP, it can be done with MLE also. Isn't it? Regards, Subhabrata Banerjee. | |
| Jun 24, 2014 at 16:18 | history | answered | user44764 | CC BY-SA 3.0 |