Draw samples from a Beta distribution.
The Beta distribution is a special case of the Dirichlet distribution,
and is related to the Gamma distribution. It has the probability
where the normalisation, B, is the beta function,
It is often seen in Bayesian inference and order statistics.
a (float or array_like of floats) – Alpha, non-negative.
b (float or array_like of floats) – Beta, non-negative.
size (int or tuple of ints, optional) – Output shape. If the given shape is, e.g., (m, n, k), then
m * n * k samples are drawn. If size is None (default),
a single value is returned if a and b are both scalars.
Otherwise, mt.broadcast(a, b).size samples are drawn.
(m, n, k)
m * n * k
chunk_size (int or tuple of int or tuple of ints, optional) – Desired chunk size on each dimension
gpu (bool, optional) – Allocate the tensor on GPU if True, False as default
dtype (data-type, optional) – Data-type of the returned tensor.
out – Drawn samples from the parameterized beta distribution.
Tensor or scalar