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Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables, with the goal of obtaining the posterior probability of the regression coefficients (as well as other parameters describing the distribution of the regressand) and ultimately allowing the out-of-sample prediction of the regressand (often labelled ) conditional on observed values of the regressors (usually ). The simplest and most widely used version of this model is the normal linear model, in which given is distributed Gaussian. In this model, and under a particular choice of prior probabilities for the parameters—so-called conjugate priors—the posterior can be found analytically. With more arbitrarily chosen priors, the posteriors gener

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rdf:type	Thing programming language
rdfs:label	Bayesian linear regression (en) Байесовская линейная регрессия (ru) Баєсова лінійна регресія (uk)
rdfs:comment	Байесовская линейная регрессия — это подход в линейной регрессии, в котором статистический анализ проводится в контексте байесовского вывода: когда регрессионная модель имеет , имеющие нормальное распределение, и, если принимается определённая форма априорного распределения, доступны явные результаты для апостериорных распределений вероятностей параметров модели. (ru) Ба́єсова ліні́йна регре́сія в статистиці — це підхід до лінійної регресії, в якому статистичний аналіз застосовується в контексті баєсового висновування. Якщо помилки регресійної моделі мають нормальний розподіл і якщо розглядається певна форма апріорного розподілу, то для апостеріорного розподілу ймовірності параметрів моделі доступні точні результати. (uk) Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables, with the goal of obtaining the posterior probability of the regression coefficients (as well as other parameters describing the distribution of the regressand) and ultimately allowing the out-of-sample prediction of the regressand (often labelled ) conditional on observed values of the regressors (usually ). The simplest and most widely used version of this model is the normal linear model, in which given is distributed Gaussian. In this model, and under a particular choice of prior probabilities for the parameters—so-called conjugate priors—the posterior can be found analytically. With more arbitrarily chosen priors, the posteriors gener (en)
differentFrom	Bayes linear statistics
dct:subject	Single-equation methods (econometrics) Bayesian inference
Wikipage page ID	7519917 (xsd:integer)
Wikipage revision ID	1103973111 (xsd:integer)
Link from a Wikipage to another Wikipage	Bayes linear statistics Bayesian inference Bayesian multivariate linear regression Single-equation methods (econometrics) Probability distribution Bayesian interpretation of kernel regularization Approximate Bayesian computation Regularized least squares Ridge regression Design matrix Inverse-gamma distribution Bayesian inference Estimation of covariance matrices Quadratic form (statistics) Gamma function Conditional expectation Conditional probability distribution Conjugate prior Likelihood function Linear function Linear regression Normal distribution Bayes theorem Posterior probability Prior probability Marginal likelihood Tikhonov regularization I.i.d. Ordinary least squares R (programming language) Spike and slab variable selection Scaled inverse chi-squared distribution Moore–Penrose pseudoinverse Normally distributed Frequentist Variational Bayes Bayesian model comparison Conditional model Prior probability distribution Posterior distribution Model evidence Monte Carlo sampling Out-of-sample dbr:B:en:R_Programming/Linear_Models dbr:Wikt:regressand
sameAs	Bayesian linear regression Bayesian linear regression Bayesian linear regression Bayesian linear regression Bayesian linear regression Bayesian linear regression
dbp:wikiPageUsesTemplate	dbt:Regression_bar dbt:Cite_book dbt:Distinguish dbt:More_footnotes dbt:Reflist dbt:Short_description dbt:Least_Squares_and_Regression_Analysis dbt:Statistics dbt:Bayesian_statistics
has abstract	Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables, with the goal of obtaining the posterior probability of the regression coefficients (as well as other parameters describing the distribution of the regressand) and ultimately allowing the out-of-sample prediction of the regressand (often labelled ) conditional on observed values of the regressors (usually ). The simplest and most widely used version of this model is the normal linear model, in which given is distributed Gaussian. In this model, and under a particular choice of prior probabilities for the parameters—so-called conjugate priors—the posterior can be found analytically. With more arbitrarily chosen priors, the posteriors generally have to be approximated. (en) Байесовская линейная регрессия — это подход в линейной регрессии, в котором статистический анализ проводится в контексте байесовского вывода: когда регрессионная модель имеет , имеющие нормальное распределение, и, если принимается определённая форма априорного распределения, доступны явные результаты для апостериорных распределений вероятностей параметров модели. (ru) Ба́єсова ліні́йна регре́сія в статистиці — це підхід до лінійної регресії, в якому статистичний аналіз застосовується в контексті баєсового висновування. Якщо помилки регресійної моделі мають нормальний розподіл і якщо розглядається певна форма апріорного розподілу, то для апостеріорного розподілу ймовірності параметрів моделі доступні точні результати. (uk)
gold:hypernym	Approach
prov:wasDerivedFrom	wikipedia-en:Bayesian_linear_regression?oldid=1103973111&ns=0
page length (characters) of wiki page	18548 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Bayesian_linear_regression
is differentFrom of	Bayes linear statistics
is Link from a Wikipage to another Wikipage of	Bayesian multivariate linear regression Probit model Bayesian interpretation of kernel regularization Coefficient of determination Empirical Bayes method Laplace's approximation Linear regression Normal distribution Bayesian Linear Regression Regression analysis List of statistics articles List of things named after Thomas Bayes Linear regression (disambiguation) Multifidelity simulation Spike-and-slab regression Bayesian linear models Bayesian regression
is Wikipage redirect of	Bayesian Linear Regression Bayesian linear models

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