numpy.random.beta¶
- numpy.random.beta(a, b, size=None)¶
The Beta distribution over [0, 1].
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
Alpha, non-negative.
b : float
Beta, non-negative.
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. Default is None, in which case a single value is returned.
Returns: out : ndarray
Array of the given shape, containing values drawn from a Beta distribution.