mxnet.gluon.probability.distributions.laplace

Laplace distribution

Classes

Laplace([loc, scale, validate_args])

Create a laplace distribution object.

class mxnet.gluon.probability.distributions.laplace.Laplace(loc=0.0, scale=1.0, validate_args=None)

Bases: Distribution

Create a laplace distribution object.

Parameters:

• loc (Tensor or scalar, default 0) – mean of the distribution.

• scale (Tensor or scalar, default 1) – scale of the distribution

broadcast_to(batch_shape)

Returns a new distribution instance with parameters expanded to batch_shape. This method calls numpy.broadcast_to on the parameters.

Parameters:

batch_shape (Tuple) – The batch shape of the desired distribution.

cdf(value)

Returns the cumulative density/mass function evaluated at value.

entropy()

Returns entropy of distribution.

icdf(value)

Returns the inverse cumulative density/mass function evaluated at value.

log_prob(value)

Compute the log likelihood of value.

Parameters:

value (Tensor) – Input data.

Returns:

Log likelihood of the input.

Return type:

Tensor

property mean

Returns the mean of the distribution.

sample(size=None)

Generate samples of size from the normal distribution parameterized by self._loc and self._scale

Parameters:

size (Tuple, Scalar, or None) – Size of samples to be generated. If size=None, the output shape will be broadcast(loc, scale).shape

Returns:

Samples from Normal distribution.

Return type:

Tensor

sample_n(size=None)

Generate samples of (batch_size + broadcast(loc, scale).shape) from the normal distribution parameterized by self._loc and self._scale

Parameters:

size (Tuple, Scalar, or None) – Size of independent batch to be generated from the distribution.

Returns:

Samples from Normal distribution.

Return type:

Tensor

property stddev

Returns the standard deviation of the distribution.

property variance

Returns the variance of the distribution.