Timeline for Connection between loss and likelihood function
Current License: CC BY-SA 3.0
7 events
| when toggle format | what | by | license | comment | |
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| May 10, 2018 at 17:56 | comment | added | James LT | Also, linear regression is only equivalent to MLE when we assume the error has a normal distribution. | |
| Aug 5, 2017 at 4:27 | comment | added | user20160 | I agree with the things you said in your answer. Regarding the last comment, I wouldn't say that MAP is a generative model. It's an approach for estimating parameters (not a model at all), and can be used with either generative or discriminative models. For example, MAP estimation could be used to fit a logistic regression model w/ priors, which is a discriminative model. | |
| Aug 3, 2017 at 16:01 | vote | accept | Pugl | ||
| Aug 3, 2017 at 16:00 | comment | added | Haitao Du | @Pegah MAP can be viewed as a generative model and logistic regression is a discriminative model, which we only focusing on getting $P(Y|X)$. this paper may help. ai.stanford.edu/~ang/papers/nips01-discriminativegenerative.pdf | |
| Aug 3, 2017 at 15:57 | comment | added | Pugl | What I mean is this: When we optimize with MAP, we maximize the posterior, i.e. we maximize likelihood x prior. In the logisitic regression context, we maximize however the likelihood function as quoted above. This likelihood function however has class conditioned on data similar to the posterior of say a bayesian classifier. Sorry if it is not understandable, not sure how to put it differently | |
| Aug 3, 2017 at 15:47 | history | edited | Haitao Du | CC BY-SA 3.0 |
added 158 characters in body
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| Aug 3, 2017 at 15:46 | history | answered | Haitao Du | CC BY-SA 3.0 |