oneapi::mkl::rng::gaussian_mv

Generates random numbers from multivariate normal distribution.

Description

The class object is used in the oneapi::mkl::rng::generate function to provide random numbers with d-variate normal (Gaussian) distribution with mean (a) and variance-covariance matrix C, where aRd ; C is dxd symmetric positive-definite matrix.

The probability density function is given by:

f_{a, C} (x) =
\frac{1}{\sqrt{\mathrm{det}(2\pi C)}}
\exp (- \frac{1}{2} (x-a)^T C^{-1} (x-a))

where x∈Rd .

Matrix C can be represented as C = TTT, where T is a lower triangular matrix - Cholesky factor of C.

API

Syntax

template<typename RealType = float, layout Layout = layout::packed, typename Method =
gaussian_mv_method::by_default>
class gaussian_mv {
public:
using method_type = Method;
using result_type = RealType;
static constexpr layout layout_type = Layout;
explicit gaussian_mv(std::uint32_t dimen, std::vector<RealType> mean,
std::vector<RealType> matrix);
explicit gaussian_mv(const param_type& pt);
std::uint32_t dimen() const;
std::vector<RealType> mean() const;
std::vector<RealType> matrix() const;
param_type param() const;
void param(const param_type& pt);
};

Devices supported: Host, CPU, and GPU.

Include Files

  • oneapi/mkl/rng.hpp

Template Parameters

Name

Description

typename RealType = float

Type of the produced values. The specific values are as follows:

float

double

layout Layout = layout::packed

Type of the matrix storage. The specific values are as follows:

layout::packed

layout::full

layout::diagonal

See brief descriptions of the methods in Distributions Template Parameter Method.

typename Method = oneapi::mkl::rng::gaussian_mv_method::by_default

Generation method. The specific values are as follows:

oneapi::mkl::rng::gaussian_mv_method::box_muller

oneapi::mkl::rng::gaussian_mv_method::box_muller2

oneapi::mkl::rng::gaussian_mv_method::icdf

See brief descriptions of the methods in Distributions Template Parameter Method.

Input Parameters

Name

Type

Description

dimen

std::uint32_t

Dimension of output random vectors

mean

std::vector< RealType> (float, double

Mean vector a of dimension d.

matrix

std::vector< RealType> (float, double

Variance-covariance matrix C.