# megengine.random.beta¶

beta(alpha, beta, size=None)

$p(x)=\frac{1}{\mathrm{~B}(\alpha, \beta)} x^{\alpha-1}(1-x)^{\beta-1} \quad \text { for } \alpha, \beta>0,$

$\mathrm{~B}(\alpha, \beta)=\int_{0}^{1} t^{\alpha-1}(1-t)^{\beta-1} d t.$

• alpha (Union[Tensor, float]) – the alpha parameter of the distribution. Must be positive.

• beta (Union[Tensor, float]) – the beta parameter of the distribution. Must be positive.

• size (Optional[Iterable[int]]) – the size of output tensor. If alpha and beta are scalars and given size is, e.g., (m, n), then the output shape is (m, n). If alpha or beta is a Tensor and given size is, e.g., (m, n), then the output shape is (m, n) + broadcast(alpha, beta).shape. Default: None.

tensor. The random variable with Beta distribution.

Return type

>>> import megengine.random as rand
>>> x = rand.beta(alpha=2, beta=1, size=(2, 2))
>>> x.numpy()
array([[0.6172312 , 0.9789006 ],
[0.50004643, 0.9775796 ]], dtype=float32)
>>> alpha = mge.Tensor([[0.5],
...                     [  3]], dtype="float32")
>>> beta = mge.Tensor([0.5,5], dtype="float32")
>>> x = rand.beta(alpha=alpha, beta=beta)
>>> x.numpy()
array([[0.0075407 , 0.1275094 ],
[0.96331763, 0.22299217]], dtype=float32)
>>> x = rand.beta(alpha=alpha, beta=beta, size=2)
>>> x.numpy()
array([[[0.46863747, 0.13819647],
[0.8646759 , 0.16014215]],

[[0.0682759 , 0.04448463],

[0.97733796, 0.19206746]]], dtype=float32)