beta(self, a, b, size=None, chunks='auto', **kwargs)
This docstring was copied from numpy.random.mtrand.RandomState.beta.
Some inconsistencies with the Dask version may exist.
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.
New code should use the beta
method of a default_rng()
instance instead; please see the :None:ref:`random-quick-start`.
Alpha, positive (>0).
Beta, positive (>0).
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.
Drawn samples from the parameterized beta distribution.
Draw samples from a Beta distribution.
Generator.beta
which should be used for new code.
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