Talk:Generative model

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Hello.

I changed

If the observed data are truly generated by the generative model, then fitting the parameters of the generative model to maximize the data likelihood is optimal.

to

is a common method.

If you still prefer optimal, please explain under wich criteria the optimality is reached. Indeed it has been shown that ML estimation has drawbacks such as overfitting. Other methods are MAP (maximun a posteriori) and averaging over posterior distribution. Dangauthier 15:58, 2 February 2007 (UTC)

I agree. However I think something stronger can be said than it simply being common, as well as some mention of ML estimates that can be directly derived from sufficient statistics vs. EM for more complicated models. I'm going to make some additions when I have some time to get my thoughts together. DaveWF 08:19, 6 February 2007 (UTC)

[edit] Descriptive model?

Generative models contrast with discriminative models, in that all the variables of a descriptive model are directly measurable.

Could this be clarified? I assume "descriptive model" refers to generative models? Perhaps words like "Generative models are descriptive models" would be helpful. Thanks, BenWilliamson 01:57, 18 October 2007 (UTC)

[edit] Observed data?

A generative model is a model for randomly generating observed data

For the newbie, can you clarify this too? If the data has already been *observed*, why do you need to generate it? I presume the idea is to take a hypothetical distribution of UNobservABLE data, and from that to generate a resulting predicted distribution of the observABLE data, then compare that predicted distribution of observABLE data with the actual distribution of the observedED data; but it would be nice to make this (or a correction of this) explicit. Mcswell 15:24, 12 November 2007 (UTC)

[edit] SVM a Generative model?

SVM does not output the posterior probabilities. In such a case why is it an example of Generative model? 121.244.161.2 (talk) 05:38, 25 April 2008 (UTC) Sunil Jagadish