xorbits.numpy.random.beta#

xorbits.numpy.random.beta(a, b, size=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 normalization, 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.

Note

New code should use the ~numpy.random.Generator.beta method of a ~numpy.random.Generator instance instead; please see the random-quick-start.

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), 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, np.broadcast(a, b).size samples are drawn.

Returns

out – Drawn samples from the parameterized beta distribution.

Return type

ndarray or scalar