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, 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)
, 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 (data-type, optional) – Data-type of the returned tensor.
- Returns
out – Drawn samples from the parameterized beta distribution.
- Return type
Tensor or scalar