numpy.random.beta¶
-
numpy.random.
beta
(a, b, size=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, 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,np.broadcast(a, b).size
samples are drawn.
Returns: - out : ndarray or scalar
Drawn samples from the parameterized beta distribution.