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 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 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 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 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 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 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>) -> Distribution<Real>

Create Gaussian distribution.

function Gaussian(μ:Expression<Real>, σ2:Real) -> Distribution<Real>

Create Gaussian distribution.

function Gaussian(μ:Real, σ2:Expression<Real>) -> Distribution<Real>

Create Gaussian distribution.

function Gaussian(μ:Real, σ2:Real) -> Distribution<Real>

Create Gaussian distribution.

function Gaussian(μ:Expression<Real>, σ2:Expression<Real>, τ2:Expression<Real>) -> Distribution<Real>

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) -> Distribution<Real>

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>) -> Distribution<Real>

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) -> Distribution<Real>

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>) -> Distribution<Real>

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) -> Distribution<Real>

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>) -> Distribution<Real>

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) -> Distribution<Real>

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(M:Expression<Real[_,_]>, U:Expression<LLT>, V:Expression<LLT>) -> Distribution<Real[_,_]>

Create matrix Gaussian distribution.

function Gaussian(M:Expression<Real[_,_]>, U:Expression<LLT>, V:LLT) -> Distribution<Real[_,_]>

Create matrix Gaussian distribution.

function Gaussian(M:Expression<Real[_,_]>, U:LLT, V:Expression<LLT>) -> Distribution<Real[_,_]>

Create matrix Gaussian distribution.

function Gaussian(M:Expression<Real[_,_]>, U:LLT, V:LLT) -> Distribution<Real[_,_]>

Create matrix Gaussian distribution.

function Gaussian(M:Real[_,_], U:Expression<LLT>, V:Expression<LLT>) -> Distribution<Real[_,_]>

Create matrix Gaussian distribution.

function Gaussian(M:Real[_,_], U:Expression<LLT>, V:LLT) -> Distribution<Real[_,_]>

Create matrix Gaussian distribution.

function Gaussian(M:Real[_,_], U:LLT, V:Expression<LLT>) -> Distribution<Real[_,_]>

Create matrix Gaussian distribution.

function Gaussian(M:Real[_,_], U:LLT, V:LLT) -> Distribution<Real[_,_]>

Create matrix Gaussian distribution.

function Gaussian(M:Expression<Real[_,_]>, U:Expression<Real[_,_]>, V:Expression<LLT>) -> Distribution<Real[_,_]>

Create matrix Gaussian distribution.

function Gaussian(M:Expression<Real[_,_]>, U:Expression<Real[_,_]>, V:LLT) -> Distribution<Real[_,_]>

Create matrix Gaussian distribution.

function Gaussian(M:Expression<Real[_,_]>, U:Real[_,_], V:Expression<LLT>) -> Distribution<Real[_,_]>

Create matrix Gaussian distribution.

function Gaussian(M:Expression<Real[_,_]>, U:Real[_,_], V:LLT) -> Distribution<Real[_,_]>

Create matrix Gaussian distribution.

function Gaussian(M:Real[_,_], U:Expression<Real[_,_]>, V:Expression<LLT>) -> Distribution<Real[_,_]>

Create matrix Gaussian distribution.

function Gaussian(M:Real[_,_], U:Expression<Real[_,_]>, V:LLT) -> Distribution<Real[_,_]>

Create matrix Gaussian distribution.

function Gaussian(M:Real[_,_], U:Real[_,_], V:Expression<LLT>) -> Distribution<Real[_,_]>

Create matrix Gaussian distribution.

function Gaussian(M:Real[_,_], U:Real[_,_], V:LLT) -> Distribution<Real[_,_]>

Create matrix Gaussian distribution.

function Gaussian(M:Expression<Real[_,_]>, U:Expression<LLT>, V:Expression<Real[_,_]>) -> Distribution<Real[_,_]>

Create matrix Gaussian distribution.

function Gaussian(M:Expression<Real[_,_]>, U:Expression<LLT>, V:Real[_,_]) -> Distribution<Real[_,_]>

Create matrix Gaussian distribution.

function Gaussian(M:Expression<Real[_,_]>, U:LLT, V:Expression<Real[_,_]>) -> Distribution<Real[_,_]>

Create matrix Gaussian distribution.

function Gaussian(M:Expression<Real[_,_]>, U:LLT, V:Real[_,_]) -> Distribution<Real[_,_]>

Create matrix Gaussian distribution.

function Gaussian(M:Real[_,_], U:Expression<LLT>, V:Expression<Real[_,_]>) -> Distribution<Real[_,_]>

Create matrix Gaussian distribution.

function Gaussian(M:Real[_,_], U:Expression<LLT>, V:Real[_,_]) -> Distribution<Real[_,_]>

Create matrix Gaussian distribution.

function Gaussian(M:Real[_,_], U:LLT, V:Expression<Real[_,_]>) -> Distribution<Real[_,_]>

Create matrix Gaussian distribution.

function Gaussian(M:Real[_,_], U:LLT, V:Real[_,_]) -> Distribution<Real[_,_]>

Create matrix Gaussian distribution.

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

Create matrix Gaussian distribution.

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

Create matrix Gaussian distribution.

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

Create matrix Gaussian distribution.

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

Create matrix Gaussian distribution.

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

Create matrix Gaussian distribution.

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

Create matrix Gaussian distribution.

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

Create matrix Gaussian distribution.

function Gaussian(M:Real[_,_], U:Real[_,_], V:Real[_,_]) -> Distribution<Real[_,_]>

Create matrix Gaussian distribution.

function Gaussian(M:Expression<Real[_,_]>, V:Expression<LLT>) -> Distribution<Real[_,_]>

Create matrix Gaussian distribution where each row is independent.

function Gaussian(M:Expression<Real[_,_]>, V:LLT) -> Distribution<Real[_,_]>

Create matrix Gaussian distribution where each row is independent.

function Gaussian(M:Real[_,_], V:Expression<LLT>) -> Distribution<Real[_,_]>

Create matrix Gaussian distribution where each row is independent.

function Gaussian(M:Expression<Real[_,_]>, V:Expression<Real[_,_]>) -> Distribution<Real[_,_]>

Create matrix Gaussian distribution where each row is independent.

function Gaussian(M:Expression<Real[_,_]>, V:Real[_,_]) -> Distribution<Real[_,_]>

Create matrix Gaussian distribution where each row is independent.

function Gaussian(M:Real[_,_], V:Expression<Real[_,_]>) -> Distribution<Real[_,_]>

Create matrix Gaussian distribution where each row is independent.

function Gaussian(μ:Expression<Real[_]>, Σ:Expression<LLT>) -> Distribution<Real[_]>

Create multivariate Gaussian distribution.

function Gaussian(μ:Expression<Real[_]>, Σ:LLT) -> Distribution<Real[_]>

Create multivariate Gaussian distribution.

function Gaussian(μ:Real[_], Σ:Expression<LLT>) -> Distribution<Real[_]>

Create multivariate Gaussian distribution.

function Gaussian(μ:Real[_], Σ:LLT) -> Distribution<Real[_]>

Create multivariate Gaussian distribution.

function Gaussian(μ:Expression<Real[_]>, Σ:Expression<Real[_,_]>) -> Distribution<Real[_]>

Create multivariate Gaussian distribution.

function Gaussian(μ:Expression<Real[_]>, Σ:Real[_,_]) -> Distribution<Real[_]>

Create multivariate Gaussian distribution.

function Gaussian(μ:Real[_], Σ:Expression<Real[_,_]>) -> Distribution<Real[_]>

Create multivariate Gaussian distribution.

function Gaussian(μ:Real[_], Σ:Real[_,_]) -> Distribution<Real[_]>

Create multivariate Gaussian distribution.

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

Create multivariate Gaussian distribution with independent and identical variance.

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

Create multivariate Gaussian distribution with independent and identical variance.

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

Create multivariate Gaussian distribution with independent and identical variance.

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

Create multivariate Gaussian distribution with independent and identical variance.

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

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) -> Distribution<Real[_]>

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>) -> Distribution<Real[_]>

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) -> Distribution<Real[_]>

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>) -> Distribution<Real[_]>

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) -> Distribution<Real[_]>

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>) -> Distribution<Real[_]>

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) -> Distribution<Real[_]>

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>) -> Distribution<Real[_]>

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) -> Distribution<Real[_]>

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>) -> Distribution<Real[_]>

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) -> Distribution<Real[_]>

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>) -> Distribution<Real[_]>

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) -> Distribution<Real[_]>

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>) -> Distribution<Real[_]>

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) -> Distribution<Real[_]>

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.