# cupy.random.multivariate_normal¶

cupy.random.multivariate_normal(mean, cov, size=None, check_valid='ignore', tol=1e-08, dtype=<class 'float'>)[source]

(experimental) Multivariate normal distribution.

Returns an array of samples drawn from the multivariate normal distribution. Its probability density function is defined as

$f(x) = \frac{1}{(2\pi|\Sigma|)^(n/2)} \exp\left(-\frac{1}{2} (x-\mu)^{\top}\Sigma^{-1}(x-\mu)\right),$
Parameters: mean (1-D array_like, of length N) – Mean of the multivariate normal distribution $$\mu$$. cov (2-D array_like, of shape (N, N)) – Covariance matrix $$\Sigma$$ of the multivariate normal distribution. It must be symmetric and positive-semidefinite for proper sampling. size (int or tuple of ints) – The shape of the array. If None, a zero-dimensional array is generated. check_valid ('warn', 'raise', 'ignore') – Behavior when the covariance matrix is not positive semidefinite. tol (float) – Tolerance when checking the singular values in covariance matrix. dtype – Data type specifier. Only numpy.float32 and numpy.float64 types are allowed. Samples drawn from the multivariate normal distribution. cupy.ndarray