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- Generative_model abstract "In probability and statistics, a generative model is a model for randomly generating observable data values, typically given some hidden parameters. It specifies a joint probability distribution over observation and label sequences. Generative models are used in machine learning for either modeling data directly (i.e., modeling observations drawn from a probability density function), or as an intermediate step to forming a conditional probability density function. A conditional distribution can be formed from a generative model through Bayes' rule.Shannon (1948) gives an example in which a table of frequencies of English word pairs is used to generate a sentence beginning with \"representing and speedily is an good\"; which is not proper English but which will increasingly approximate it as the table is moved from word pairs to word triplets etc.Generative models contrast with discriminative models, in that a generative model is a full probabilistic model of all variables, whereas a discriminative model provides a model only for the target variable(s) conditional on the observed variables. Thus a generative model can be used, for example, to simulate (i.e. generate) values of any variable in the model, whereas a discriminative model allows only sampling of the target variables conditional on the observed quantities. Despite the fact that discriminative models do not need to model the distribution of the observed variables, they cannot generally express more complex relationships between the observed and target variables. They don't necessarily perform better than generative models at classification and regression tasks. In modern applications the two classes are seen as complementary or as different views of the same procedure.Examples of generative models include: Gaussian mixture model and other types of mixture model Hidden Markov model Probabilistic context-free grammar Naive Bayes Averaged one-dependence estimators Latent Dirichlet allocation Restricted Boltzmann machineIf the observed data are truly sampled from the generative model, then fitting the parameters of the generative model to 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 above), although application-specific details will ultimately dictate which approach is most suitable in any particular case.".
- Generative_model wikiPageExternalLink shannon1948.pdf.
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- Generative_model wikiPageWikiLink Averaged_one-dependence_estimators.
- Generative_model wikiPageWikiLink Bayes_rule.
- Generative_model wikiPageWikiLink Bell_System_Technical_Journal.
- Generative_model wikiPageWikiLink Category:Machine_learning.
- Generative_model wikiPageWikiLink Category:Probabilistic_models.
- Generative_model wikiPageWikiLink Category:Statistical_models.
- Generative_model wikiPageWikiLink Claude_Shannon.
- Generative_model wikiPageWikiLink Conditional_probability.
- Generative_model wikiPageWikiLink Discriminative_model.
- Generative_model wikiPageWikiLink Graphical_model.
- Generative_model wikiPageWikiLink Hidden_Markov_model.
- Generative_model wikiPageWikiLink Joint_probability_distribution.
- Generative_model wikiPageWikiLink Latent_Dirichlet_allocation.
- Generative_model wikiPageWikiLink Latent_variable.
- Generative_model wikiPageWikiLink Machine_learning.
- Generative_model wikiPageWikiLink Maximum_likelihood.
- Generative_model wikiPageWikiLink Mixture_model.
- Generative_model wikiPageWikiLink Naive_Bayes_classifier.
- Generative_model wikiPageWikiLink Probability.
- Generative_model wikiPageWikiLink Probability_density_function.
- Generative_model wikiPageWikiLink Regression_analysis.
- Generative_model wikiPageWikiLink Restricted_Boltzmann_machine.
- Generative_model wikiPageWikiLink Statistical_classification.
- Generative_model wikiPageWikiLink Statistics.
- Generative_model wikiPageWikiLink Stochastic_context-free_grammar.
- Generative_model wikiPageWikiLinkText "Generative model".
- Generative_model wikiPageWikiLinkText "generative model".
- Generative_model wikiPageWikiLinkText "generative statistical models".
- Generative_model wikiPageWikiLinkText "generative".
- Generative_model wikiPageWikiLinkText "generatively".
- Generative_model wikiPageUsesTemplate Template:Context.
- Generative_model wikiPageUsesTemplate Template:Portal.
- Generative_model wikiPageUsesTemplate Template:Reflist.
- Generative_model wikiPageUsesTemplate Template:Statistics.
- Generative_model subject Category:Machine_learning.
- Generative_model subject Category:Probabilistic_models.
- Generative_model subject Category:Statistical_models.
- Generative_model hypernym Model.
- Generative_model type Model.
- Generative_model type Person.
- Generative_model type Model.
- Generative_model type Page.
- Generative_model comment "In probability and statistics, a generative model is a model for randomly generating observable data values, typically given some hidden parameters. It specifies a joint probability distribution over observation and label sequences. Generative models are used in machine learning for either modeling data directly (i.e., modeling observations drawn from a probability density function), or as an intermediate step to forming a conditional probability density function.".
- Generative_model label "Generative model".
- Generative_model sameAs Q5532625.
- Generative_model sameAs Modelo_generador.
- Generative_model sameAs m.04js0s.
- Generative_model sameAs Породжувальна_модель.
- Generative_model sameAs Q5532625.
- Generative_model sameAs 生成模型.
- Generative_model wasDerivedFrom Generative_model?oldid=699037245.
- Generative_model isPrimaryTopicOf Generative_model.