Generative Models
Created: January 2, 2021 11:00 AM
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Last Edited: June 20, 2021 12:20 PM
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URL: https://en.wikipedia.org/wiki/Generative_model
In statistical classification, two main approaches are called the generative approach and the discriminative approach. These compute classifiers by different approaches, differing in the degree of statistical modelling. Terminology is inconsistent,[a] but three major types can be distinguished, following Jebara (2004):
Given an observable variable X and a target variable Y, a generative model is a statistical model of the joint probability distribution on X × Y, {\displaystyle P(X,Y)}{\displaystyle P(X,Y)};[1]
A discriminative model is a model of the conditional probability of the target Y, given an observation x, symbolically, {\displaystyle P(Y|X=x)}{\displaystyle P(Y|X=x)}; and
Classifiers computed without using a probability model are also referred to loosely as "discriminative".
The distinction between these last two classes is not consistently made;[2] Jebara (2004) refers to these three classes as generative learning, conditional learning, and discriminative learning, but Ng & Jordan (2002) only distinguish two classes, calling them generative classifiers (joint distribution) and discriminative classifiers (conditional distribution or no distribution), not distinguishing between the latter two classes.[3] Analogously, a classifier based on a generative model is a generative classifier, while a classifier based on a discriminative model is a discriminative classifier, though this term also refers to classifiers that are not based on a model.