# Gaussian

final 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.
Gaussian Create multivariate Gaussian distribution.
Gaussian Create multivariate Gaussian distribution.
Gaussian Create multivariate Gaussian distribution.
Gaussian Create matrix Gaussian distribution where each column is independent.
Gaussian Create matrix Gaussian distribution where each column is independent.
Gaussian Create matrix Gaussian distribution where each column is independent.
Gaussian Create matrix Gaussian distribution where each column is independent.
Gaussian Create matrix Gaussian distribution where each column is independent.
Gaussian Create matrix Gaussian distribution where each column is independent.
Gaussian Create matrix Gaussian distribution where each column is independent.
Gaussian Create matrix Gaussian distribution where each column is independent.
Gaussian Create matrix Gaussian distribution where each element is independent.
Gaussian Create matrix Gaussian distribution where each element is independent.
Gaussian Create matrix Gaussian distribution where each element is independent.
Gaussian Create matrix Gaussian distribution where each element 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 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.

### 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.

function Gaussian(μ:Expression<Real[_]>, σ2:Real) -> IdenticalGaussian

Create multivariate Gaussian distribution.

function Gaussian(μ:Real[_], σ2:Expression<Real>) -> IdenticalGaussian

Create multivariate Gaussian distribution.

function Gaussian(μ:Real[_], σ2:Real) -> IdenticalGaussian

Create multivariate Gaussian distribution.

function Gaussian(M:Expression<Real[_,_]>, U:Expression<Real[_,_]>, σ2:Expression<Real[_]>) -> IndependentColumnMatrixGaussian

Create matrix Gaussian distribution where each column is independent.

function Gaussian(M:Expression<Real[_,_]>, U:Expression<Real[_,_]>, σ2:Real[_]) -> IndependentColumnMatrixGaussian

Create matrix Gaussian distribution where each column is independent.

function Gaussian(M:Expression<Real[_,_]>, U:Real[_,_], σ2:Expression<Real[_]>) -> IndependentColumnMatrixGaussian

Create matrix Gaussian distribution where each column is independent.

function Gaussian(M:Expression<Real[_,_]>, U:Real[_,_], σ2:Real[_]) -> IndependentColumnMatrixGaussian

Create matrix Gaussian distribution where each column is independent.

function Gaussian(M:Real[_,_], U:Expression<Real[_,_]>, σ2:Expression<Real[_]>) -> IndependentColumnMatrixGaussian

Create matrix Gaussian distribution where each column is independent.

function Gaussian(M:Real[_,_], U:Expression<Real[_,_]>, σ2:Real[_]) -> IndependentColumnMatrixGaussian

Create matrix Gaussian distribution where each column is independent.

function Gaussian(M:Real[_,_], U:Real[_,_], σ2:Expression<Real[_]>) -> IndependentColumnMatrixGaussian

Create matrix Gaussian distribution where each column is independent.

function Gaussian(M:Real[_,_], U:Real[_,_], σ2:Real[_]) -> IndependentColumnMatrixGaussian

Create matrix Gaussian distribution where each column is independent.

function Gaussian(M:Expression<Real[_,_]>, σ2:Expression<Real[_]>) -> IndependentMatrixGaussian

Create matrix Gaussian distribution where each element is independent.

function Gaussian(M:Expression<Real[_,_]>, σ2:Real[_]) -> IndependentMatrixGaussian

Create matrix Gaussian distribution where each element is independent.

function Gaussian(M:Real[_,_], σ2:Expression<Real[_]>) -> IndependentMatrixGaussian

Create matrix Gaussian distribution where each element is independent.

function Gaussian(M:Real[_,_], σ2:Real[_]) -> IndependentMatrixGaussian

Create matrix Gaussian distribution where each element 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<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<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<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.