class

Kumaraswamy

extendsDistribution

Kumaraswamy(concentration1: Tensor | float, concentration0: Tensor | float, validate_args: bool | None = None)

source

Kumaraswamy distribution on $(0, 1)$ .

Two-parameter continuous distribution on the unit interval that mimics the shapes of the lucid.distributions.Beta distribution but has a closed-form CDF and inverse CDF. This makes it a popular choice in variational autoencoders and normalising flows where cheap, exact reparameterised sampling is needed and no special functions are required.

Parameters

concentration1Tensor or float

First shape parameter

a > 0

concentration0Tensor or float

Second shape parameter

b > 0

validate_argsbool= None

If True, validate parameter constraints at construction time.

Notes

Probability density on $x \in (0, 1)$ :

p(x; a, b) = a\, b\, x^{a - 1} (1 - x^a)^{b - 1}

Cumulative distribution (closed form):

F(x; a, b) = 1 - (1 - x^a)^b

Inverse CDF (used by rsample):

F^{-1}(u; a, b) = (1 - (1 - u)^{1/b})^{1/a}

Moments are expressible in closed form via the Beta function:

\mathbb{E}[X^n] = b\, B(1 + n/a,\, b)

Special cases:

$a = 1$ → $\mathrm{Beta}(1, b)$
$b = 1$ → $\mathrm{Beta}(a, 1)$

Unlike Beta, Kumaraswamy is not a member of the exponential family and lacks an analytic normalising integral for sums. Its main advantage is the lack of any reliance on the gamma function in forward or backward passes.

Examples

>>> import lucid
>>> from lucid.distributions import Kumaraswamy
>>> d = Kumaraswamy(concentration1=2.0, concentration0=5.0)
>>> d.rsample((4,))
Tensor([...])
>>> d.log_prob(lucid.tensor(0.3))
Tensor(...)

Methods (6)

dunder

init

→None

__init__(concentration1: Tensor | float, concentration0: Tensor | float, validate_args: bool | None = None)

source

Initialise a Kumaraswamy distribution.

Parameters

concentration1Tensor | float

First shape parameter

a > 0

(controls the left tail).

concentration0Tensor | float

Second shape parameter

b > 0

(controls the right tail).

validate_argsbool | None= None

If True, validate parameter constraints at construction time.

prop

mean

→Tensor

mean: Tensor

source

E[X] = b · B(1 + 1/a, b) expressed via lgamma.

prop

variance

→Tensor

variance: Tensor

source

Var[X] = E[X²] − E[X]² where E[X²] = b · B(1 + 2/a, b).

rsample

→Tensor

rsample(sample_shape: tuple[int, ...] = ())

source

Reparameterised sample via the icdf: x = (1 − (1 − U)^(1/b))^(1/a).

log_prob

→Tensor

log_prob(value: Tensor)

source

Log-probability density of the Kumaraswamy distribution.

Parameters

valueTensor

Point(s)

x \in (0, 1)

at which to evaluate the density.

Returns

Tensor

Log-density $\log a + \log b + (a-1)\log x + (b-1)\log(1-x^a)$ .

entropy

→Tensor

entropy()

source

Entropy via the Beta-distributed auxiliary variable Y = X^a.

If X ~ Kumaraswamy(a, b) then Y = X^a ~ Beta(1, b), giving: E[log X] = (1/a)(ψ(1) − ψ(b+1)) = −(1/a)(γ + ψ(b+1)) E[log(1−X^a)] = ψ(b) − ψ(b+1) = −1/b

H = −log a − log b − (a−1)·E[log X] − (b−1)·E[log(1−X^a)] = −log a − log b + (1 − 1/a)(γ + ψ(b+1)) + (1 − 1/b)