mxnet.np.random.beta¶
-
beta
(a, b, size=None, dtype=None, device=None)¶ 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, positive (>0).
b (float or array_like of floats) – Beta, positive (>0).
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,np.broadcast(a, b).size
samples are drawn.dtype ({'float16', 'float32', 'float64'}, optional) – Data type of output samples. Default is ‘float32’. Dtype ‘float32’ or ‘float64’ is strongly recommended, since lower precision might lead to out of range issue.
device (Device, optional) – Device context of output. Default is current device.
Notes
To use this operator with scalars as input, please run
npx.set_np()
first.- Returns
out – Drawn samples from the parameterized beta distribution.
- Return type
ndarray or scalar