Editing Generative model (section)

== Types ==

=== Generative models ===

Types of generative models are:

* [[Gaussian mixture model]] (and other types of [[mixture model]])
* [[Hidden Markov model]]
* [[Stochastic context-free grammar|Probabilistic context-free grammar]]
* [[Bayesian network]] (e.g. [[Naive bayes]], [[Autoregressive model]])
* [[Averaged one-dependence estimators]]
* [[Latent Dirichlet allocation]]
* [[Boltzmann machine]] (e.g. [[Restricted Boltzmann machine]], [[Deep belief network]])
* [[Autoencoder#Variational autoencoder (VAE)|Variational autoencoder]]
* [[Generative adversarial network]]
* [[Flow-based generative model]]
* [[Energy based model]]
* [[Diffusion model]]

If the observed data are truly sampled from the generative model, then fitting the parameters of the generative model to [[maximum likelihood estimation|maximize the data likelihood]] is a common method. However, since most statistical models are only approximations to the ''true'' distribution, if the model's application is to infer about a subset of variables conditional on known values of others, then it can be argued that the approximation makes more assumptions than are necessary to solve the problem at hand. In such cases, it can be more accurate to model the conditional density functions directly using a [[discriminative model]] (see below), although application-specific details will ultimately dictate which approach is most suitable in any particular case.

=== Discriminative models ===

* [[k-nearest neighbors algorithm]]
* [[Logistic regression]]
* [[Support Vector Machines]]
* [[Decision Tree Learning]]
* [[Random Forest]]
* [[Maximum-entropy Markov model]]s
* [[Conditional random field]]s