multivariate Gaussian distribution

GPTKB entity

Statements (52)
Predicate Object
gptkbp:instanceOf gptkb:organization
gptkbp:alsoKnownAs gptkb:multivariate_normal_distribution
gptkbp:characteristic exp(i t^T μ - 1/2 t^T Σ t)
gptkbp:conditionalDistribution gptkb:multivariate_normal_distribution
gptkbp:covarianceMatrixSymbol Σ
gptkbp:cumulantGeneratingFunction μ^T t + 1/2 t^T Σ t
gptkbp:definedIn n-dimensional real vector space
gptkbp:densityFunctionDependsOn covariance matrix
mean vector
gptkbp:entropyFormula (1/2) ln((2πe)^k |Σ|)
gptkbp:firstDescribed gptkb:Carl_Friedrich_Gauss
gptkbp:hasDensityFunction yes
gptkbp:hasSpecialCase gptkb:exponential_family
gptkb:elliptical_distribution
https://www.w3.org/2000/01/rdf-schema#label multivariate Gaussian distribution
gptkbp:ifCovarianceDiagonal independent components
gptkbp:ifCovarianceIdentity gptkb:standard_multivariate_normal_distribution
gptkbp:ifCovarianceSingular gptkb:degenerate_distribution
gptkbp:ifMeanZero centered distribution
gptkbp:marginalDistribution gptkb:univariate_normal_distribution
gptkbp:maximumEntropyDistribution given mean and covariance
gptkbp:meanVectorSymbol μ
gptkbp:moments all moments exist
gptkbp:parameter covariance matrix
mean vector
gptkbp:pdfFormula (2π)^{-k/2} |Σ|^{-1/2} exp(-1/2 (x-μ)^T Σ^{-1} (x-μ))
gptkbp:relatedTo gptkb:Hotelling's_T-squared_distribution
gptkb:Mahalanobis_distance
gptkb:normal_distribution
gptkb:Gaussian_copula
gptkb:Wishart_distribution
gptkb:multivariate_t-distribution
gptkb:Gaussian_process
Bayesian statistics
linear regression
covariance structure
random vector
gptkbp:supports entire n-dimensional real space
gptkbp:usedFor gptkb:principal_component_analysis
gptkb:Gaussian_processes
gptkb:Gaussian_mixture_models
Kalman filtering
linear discriminant analysis
modeling correlated variables
gptkbp:usedIn gptkb:machine_learning
gptkb:signal_processing
statistics
Bayesian inference
pattern recognition
gptkbp:bfsParent gptkb:multivariate_normal_distribution
gptkb:Machine_Learning_by_Andrew_Ng
gptkbp:bfsLayer 6