Type Kumaraswamy
Namespace tensorflow.contrib.distributions
Parent TransformedDistribution
Interfaces IKumaraswamy
Kumaraswamy distribution. The Kumaraswamy distribution is defined over the `(0, 1)` interval using
parameters
`concentration1` (aka "alpha") and `concentration0` (aka "beta"). It has a
shape similar to the Beta distribution, but is reparameterizeable. #### Mathematical Details The probability density function (pdf) is, ```none
pdf(x; alpha, beta) = alpha * beta * x**(alpha - 1) * (1 - x**alpha)**(beta -
1)
``` where: * `concentration1 = alpha`,
* `concentration0 = beta`, Distribution parameters are automatically broadcast in all functions; see
examples for details. #### Examples
Show Example
# Create a batch of three Kumaraswamy distributions. alpha = [1, 2, 3] beta = [1, 2, 3] dist = Kumaraswamy(alpha, beta) dist.sample([4, 5]) # Shape [4, 5, 3] # `x` has three batch entries, each with two samples. x = [[.1,.4,.5], [.2,.3,.5]] # Calculate the probability of each pair of samples under the corresponding # distribution in `dist`. dist.prob(x) # Shape [2, 3]
Properties
- allow_nan_stats
- allow_nan_stats_dyn
- batch_shape
- batch_shape_dyn
- bijector
- bijector_dyn
- concentration0
- concentration0_dyn
- concentration1
- concentration1_dyn
- distribution
- distribution_dyn
- dtype
- dtype_dyn
- event_shape
- event_shape_dyn
- name
- name_dyn
- parameters
- parameters_dyn
- PythonObject
- reparameterization_type
- reparameterization_type_dyn
- validate_args
- validate_args_dyn
Public properties
object allow_nan_stats get;
object allow_nan_stats_dyn get;
TensorShape batch_shape get;
object batch_shape_dyn get;
object bijector get;
object bijector_dyn get;
Tensor concentration0 get;
Concentration parameter associated with a `0` outcome.
object concentration0_dyn get;
Concentration parameter associated with a `0` outcome.
Tensor concentration1 get;
Concentration parameter associated with a `1` outcome.
object concentration1_dyn get;
Concentration parameter associated with a `1` outcome.