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 probabilitydistribution 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.