mars.tensor.random.beta¶

mars.tensor.random.
beta
(a, b, size=None, chunk_size=None, gpu=None, dtype=None)[source]¶ 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 distribution function
\[f(x; a,b) = \frac{1}{B(\alpha, \beta)} x^{\alpha  1} (1  x)^{\beta  1},\]where the normalisation, B, is the beta function,
\[B(\alpha, \beta) = \int_0^1 t^{\alpha  1} (1  t)^{\beta  1} dt.\]It is often seen in Bayesian inference and order statistics.
 Parameters
a (float or array_like of floats) – Alpha, nonnegative.
b (float or array_like of floats) – Beta, nonnegative.
size (int or tuple of ints, optional) – Output shape. If the given shape is, e.g.,
(m, n, k)
, thenm * n * k
samples are drawn. If size isNone
(default), a single value is returned ifa
andb
are both scalars. Otherwise,mt.broadcast(a, b).size
samples are drawn.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 (datatype, optional) – Datatype of the returned tensor.
 Returns
out – Drawn samples from the parameterized beta distribution.
 Return type
Tensor or scalar