Gaussian
class Gaussian(μ:Expression<Real>, σ2:Expression<Real>) < Distribution<Real>
Gaussian distribution.
Factory Functions
Name | Description |
---|---|
Gaussian | Create Gaussian distribution. |
Gaussian | Create Gaussian distribution. |
Gaussian | Create Gaussian distribution. |
Gaussian | Create Gaussian distribution. |
Gaussian | Create multivariate Gaussian distribution with independent and identical variance. |
Gaussian | Create multivariate Gaussian distribution with independent and identical variance. |
Gaussian | Create multivariate Gaussian distribution with independent and identical variance. |
Gaussian | Create multivariate Gaussian distribution with independent and identical variance. |
Gaussian | Create matrix Gaussian distribution where each row is independent. |
Gaussian | Create matrix Gaussian distribution where each row is independent. |
Gaussian | Create matrix Gaussian distribution where each row is independent. |
Gaussian | Create matrix Gaussian distribution where each row is independent. |
Gaussian | Create matrix Gaussian distribution where each row is independent. |
Gaussian | Create matrix Gaussian distribution where each row is independent. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create matrix Gaussian distribution. |
Gaussian | Create multivariate Gaussian distribution. |
Gaussian | Create multivariate Gaussian distribution. |
Gaussian | Create multivariate Gaussian distribution. |
Gaussian | Create multivariate Gaussian distribution. |
Gaussian | Create multivariate Gaussian distribution. |
Gaussian | Create multivariate Gaussian distribution. |
Gaussian | Create multivariate Gaussian distribution. |
Gaussian | Create multivariate Gaussian distribution. |
Gaussian | Create Gaussian distribution where the variance is given as a product of two scalars. |
Gaussian | Create Gaussian distribution where the variance is given as a product of two scalars. |
Gaussian | Create Gaussian distribution where the variance is given as a product of two scalars. |
Gaussian | Create Gaussian distribution where the variance is given as a product of two scalars. |
Gaussian | Create Gaussian distribution where the variance is given as a product of two scalars. |
Gaussian | Create Gaussian distribution where the variance is given as a product of two scalars. |
Gaussian | Create Gaussian distribution where the variance is given as a product of two scalars. |
Gaussian | Create Gaussian distribution where the variance is given as a product of two scalars. |
Gaussian | Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. |
Gaussian | Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. |
Gaussian | Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. |
Gaussian | Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. |
Gaussian | Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. |
Gaussian | Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. |
Gaussian | Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. |
Gaussian | Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. |
Gaussian | Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. |
Gaussian | Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. |
Gaussian | Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. |
Gaussian | Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. |
Gaussian | Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. |
Gaussian | Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. |
Gaussian | Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. |
Gaussian | Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. |
Member Variables
Name | Description |
---|---|
μ:Expression<Real> | Mean. |
σ2:Expression<Real> | Variance. |
Factory Function Details
function Gaussian(μ:Expression<Real>, σ2:Expression<Real>) -> Gaussian
Create Gaussian distribution.
function Gaussian(μ:Expression<Real>, σ2:Real) -> Gaussian
Create Gaussian distribution.
function Gaussian(μ:Real, σ2:Expression<Real>) -> Gaussian
Create Gaussian distribution.
function Gaussian(μ:Real, σ2:Real) -> Gaussian
Create Gaussian distribution.
function Gaussian(μ:Expression<Real[_]>, σ2:Expression<Real>) -> IdenticalGaussian
Create multivariate Gaussian distribution with independent and identical variance.
function Gaussian(μ:Expression<Real[_]>, σ2:Real) -> IdenticalGaussian
Create multivariate Gaussian distribution with independent and identical variance.
function Gaussian(μ:Real[_], σ2:Expression<Real>) -> IdenticalGaussian
Create multivariate Gaussian distribution with independent and identical variance.
function Gaussian(μ:Real[_], σ2:Real) -> IdenticalGaussian
Create multivariate Gaussian distribution with independent and identical variance.
function Gaussian(M:Expression<Real[_,_]>, V:Expression<LLT>) -> IndependentRowMatrixGaussian
Create matrix Gaussian distribution where each row is independent.
function Gaussian(M:Expression<Real[_,_]>, V:LLT) -> IndependentRowMatrixGaussian
Create matrix Gaussian distribution where each row is independent.
function Gaussian(M:Real[_,_], V:Expression<LLT>) -> IndependentRowMatrixGaussian
Create matrix Gaussian distribution where each row is independent.
function Gaussian(M:Expression<Real[_,_]>, V:Expression<Real[_,_]>) -> IndependentRowMatrixGaussian
Create matrix Gaussian distribution where each row is independent.
function Gaussian(M:Expression<Real[_,_]>, V:Real[_,_]) -> IndependentRowMatrixGaussian
Create matrix Gaussian distribution where each row is independent.
function Gaussian(M:Real[_,_], V:Expression<Real[_,_]>) -> IndependentRowMatrixGaussian
Create matrix Gaussian distribution where each row is independent.
function Gaussian(M:Expression<Real[_,_]>, U:Expression<LLT>, V:Expression<LLT>) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(M:Expression<Real[_,_]>, U:Expression<LLT>, V:LLT) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(M:Expression<Real[_,_]>, U:LLT, V:Expression<LLT>) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(M:Expression<Real[_,_]>, U:LLT, V:LLT) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(M:Real[_,_], U:Expression<LLT>, V:Expression<LLT>) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(M:Real[_,_], U:Expression<LLT>, V:LLT) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(M:Real[_,_], U:LLT, V:Expression<LLT>) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(M:Real[_,_], U:LLT, V:LLT) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(M:Expression<Real[_,_]>, U:Expression<Real[_,_]>, V:Expression<LLT>) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(M:Expression<Real[_,_]>, U:Expression<Real[_,_]>, V:LLT) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(M:Expression<Real[_,_]>, U:Real[_,_], V:Expression<LLT>) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(M:Expression<Real[_,_]>, U:Real[_,_], V:LLT) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(M:Real[_,_], U:Expression<Real[_,_]>, V:Expression<LLT>) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(M:Real[_,_], U:Expression<Real[_,_]>, V:LLT) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(M:Real[_,_], U:Real[_,_], V:Expression<LLT>) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(M:Real[_,_], U:Real[_,_], V:LLT) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(M:Expression<Real[_,_]>, U:Expression<LLT>, V:Expression<Real[_,_]>) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(M:Expression<Real[_,_]>, U:Expression<LLT>, V:Real[_,_]) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(M:Expression<Real[_,_]>, U:LLT, V:Expression<Real[_,_]>) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(M:Expression<Real[_,_]>, U:LLT, V:Real[_,_]) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(M:Real[_,_], U:Expression<LLT>, V:Expression<Real[_,_]>) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(M:Real[_,_], U:Expression<LLT>, V:Real[_,_]) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(M:Real[_,_], U:LLT, V:Expression<Real[_,_]>) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(M:Real[_,_], U:LLT, V:Real[_,_]) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(M:Expression<Real[_,_]>, U:Expression<Real[_,_]>, V:Expression<Real[_,_]>) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(M:Expression<Real[_,_]>, U:Expression<Real[_,_]>, V:Real[_,_]) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(M:Expression<Real[_,_]>, U:Real[_,_], V:Expression<Real[_,_]>) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(M:Expression<Real[_,_]>, U:Real[_,_], V:Real[_,_]) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(M:Real[_,_], U:Expression<Real[_,_]>, V:Expression<Real[_,_]>) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(M:Real[_,_], U:Expression<Real[_,_]>, V:Real[_,_]) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(M:Real[_,_], U:Real[_,_], V:Expression<Real[_,_]>) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(M:Real[_,_], U:Real[_,_], V:Real[_,_]) -> MatrixGaussian
Create matrix Gaussian distribution.
function Gaussian(μ:Expression<Real[_]>, Σ:Expression<LLT>) -> MultivariateGaussian
Create multivariate Gaussian distribution.
function Gaussian(μ:Expression<Real[_]>, Σ:LLT) -> MultivariateGaussian
Create multivariate Gaussian distribution.
function Gaussian(μ:Real[_], Σ:Expression<LLT>) -> MultivariateGaussian
Create multivariate Gaussian distribution.
function Gaussian(μ:Real[_], Σ:LLT) -> MultivariateGaussian
Create multivariate Gaussian distribution.
function Gaussian(μ:Expression<Real[_]>, Σ:Expression<Real[_,_]>) -> MultivariateGaussian
Create multivariate Gaussian distribution.
function Gaussian(μ:Expression<Real[_]>, Σ:Real[_,_]) -> MultivariateGaussian
Create multivariate Gaussian distribution.
function Gaussian(μ:Real[_], Σ:Expression<Real[_,_]>) -> MultivariateGaussian
Create multivariate Gaussian distribution.
function Gaussian(μ:Real[_], Σ:Real[_,_]) -> MultivariateGaussian
Create multivariate Gaussian distribution.
function Gaussian(μ:Expression<Real>, σ2:Expression<Real>, τ2:Expression<Real>) -> ScalarGaussian
Create Gaussian distribution where the variance is given as a product of two scalars. This is usually used for establishing a normal-inverse-gamma distribution, where one of the arguments is inverse-gamma distributed.
function Gaussian(μ:Expression<Real>, σ2:Expression<Real>, τ2:Real) -> ScalarGaussian
Create Gaussian distribution where the variance is given as a product of two scalars. This is usually used for establishing a normal-inverse-gamma distribution, where one of the arguments is inverse-gamma distributed.
function Gaussian(μ:Expression<Real>, σ2:Real, τ2:Expression<Real>) -> ScalarGaussian
Create Gaussian distribution where the variance is given as a product of two scalars. This is usually used for establishing a normal-inverse-gamma distribution, where one of the arguments is inverse-gamma distributed.
function Gaussian(μ:Expression<Real>, σ2:Real, τ2:Real) -> ScalarGaussian
Create Gaussian distribution where the variance is given as a product of two scalars. This is usually used for establishing a normal-inverse-gamma distribution, where one of the arguments is inverse-gamma distributed.
function Gaussian(μ:Real, σ2:Expression<Real>, τ2:Expression<Real>) -> ScalarGaussian
Create Gaussian distribution where the variance is given as a product of two scalars. This is usually used for establishing a normal-inverse-gamma distribution, where one of the arguments is inverse-gamma distributed.
function Gaussian(μ:Real, σ2:Expression<Real>, τ2:Real) -> ScalarGaussian
Create Gaussian distribution where the variance is given as a product of two scalars. This is usually used for establishing a normal-inverse-gamma distribution, where one of the arguments is inverse-gamma distributed.
function Gaussian(μ:Real, σ2:Real, τ2:Expression<Real>) -> ScalarGaussian
Create Gaussian distribution where the variance is given as a product of two scalars. This is usually used for establishing a normal-inverse-gamma distribution, where one of the arguments is inverse-gamma distributed.
function Gaussian(μ:Real, σ2:Real, τ2:Real) -> ScalarGaussian
Create Gaussian distribution where the variance is given as a product of two scalars. This is usually used for establishing a normal-inverse-gamma distribution, where one of the arguments is inverse-gamma distributed.
function Gaussian(μ:Expression<Real[_]>, Σ:Expression<LLT>, σ2:Expression<Real>) -> ScalarMultivariateGaussian
Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. This is usually used for establishing a multivariate normal-inverse-gamma, where the final argument is inverse-gamma distributed.
function Gaussian(μ:Expression<Real[_]>, Σ:Expression<LLT>, σ2:Real) -> ScalarMultivariateGaussian
Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. This is usually used for establishing a multivariate normal-inverse-gamma, where the final argument is inverse-gamma distributed.
function Gaussian(μ:Expression<Real[_]>, Σ:LLT, σ2:Expression<Real>) -> ScalarMultivariateGaussian
Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. This is usually used for establishing a multivariate normal-inverse-gamma, where the final argument is inverse-gamma distributed.
function Gaussian(μ:Expression<Real[_]>, Σ:LLT, σ2:Real) -> ScalarMultivariateGaussian
Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. This is usually used for establishing a multivariate normal-inverse-gamma, where the final argument is inverse-gamma distributed.
function Gaussian(μ:Real[_], Σ:Expression<LLT>, σ2:Expression<Real>) -> ScalarMultivariateGaussian
Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. This is usually used for establishing a multivariate normal-inverse-gamma, where the final argument is inverse-gamma distributed.
function Gaussian(μ:Real[_], Σ:Expression<LLT>, σ2:Real) -> ScalarMultivariateGaussian
Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. This is usually used for establishing a multivariate normal-inverse-gamma, where the final argument is inverse-gamma distributed.
function Gaussian(μ:Real[_], Σ:LLT, σ2:Expression<Real>) -> ScalarMultivariateGaussian
Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. This is usually used for establishing a multivariate normal-inverse-gamma, where the final argument is inverse-gamma distributed.
function Gaussian(μ:Real[_], Σ:LLT, σ2:Real) -> ScalarMultivariateGaussian
Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. This is usually used for establishing a multivariate normal-inverse-gamma, where the final argument is inverse-gamma distributed.
function Gaussian(μ:Expression<Real[_]>, Σ:Expression<Real[_,_]>, σ2:Expression<Real>) -> ScalarMultivariateGaussian
Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. This is usually used for establishing a multivariate normal-inverse-gamma, where the final argument is inverse-gamma distributed.
function Gaussian(μ:Expression<Real[_]>, Σ:Expression<Real[_,_]>, σ2:Real) -> ScalarMultivariateGaussian
Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. This is usually used for establishing a multivariate normal-inverse-gamma, where the final argument is inverse-gamma distributed.
function Gaussian(μ:Expression<Real[_]>, Σ:Real[_,_], σ2:Expression<Real>) -> ScalarMultivariateGaussian
Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. This is usually used for establishing a multivariate normal-inverse-gamma, where the final argument is inverse-gamma distributed.
function Gaussian(μ:Expression<Real[_]>, Σ:Real[_,_], σ2:Real) -> ScalarMultivariateGaussian
Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. This is usually used for establishing a multivariate normal-inverse-gamma, where the final argument is inverse-gamma distributed.
function Gaussian(μ:Real[_], Σ:Expression<Real[_,_]>, σ2:Expression<Real>) -> ScalarMultivariateGaussian
Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. This is usually used for establishing a multivariate normal-inverse-gamma, where the final argument is inverse-gamma distributed.
function Gaussian(μ:Real[_], Σ:Expression<Real[_,_]>, σ2:Real) -> ScalarMultivariateGaussian
Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. This is usually used for establishing a multivariate normal-inverse-gamma, where the final argument is inverse-gamma distributed.
function Gaussian(μ:Real[_], Σ:Real[_,_], σ2:Expression<Real>) -> ScalarMultivariateGaussian
Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. This is usually used for establishing a multivariate normal-inverse-gamma, where the final argument is inverse-gamma distributed.
function Gaussian(μ:Real[_], Σ:Real[_,_], σ2:Real) -> ScalarMultivariateGaussian
Create multivariate Gaussian distribution where the covariance is given as a matrix multiplied by a scalar. This is usually used for establishing a multivariate normal-inverse-gamma, where the final argument is inverse-gamma distributed.