Skip to content

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.