tf.contrib.distributions.TransformedDistribution.sample_n(n, seed=None, name='sample_n') Generate n samples. Additional documentation from TransformedDistribution: Samples from the base distribution and then passes through the transform. Args: n: Scalar Tensor of type int32 or int64, the number of observations to sample. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with a prepended dimension (n,). Raises: TypeError: if n is not an integer

tf.contrib.distributions.TransformedDistribution.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified shape. Note that a call to sample() without arguments will generate a single sample. Args: sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with prepended dimensions sample_shape.

tf.contrib.distributions.TransformedDistribution.prob(value, name='prob') Probability density/mass function (depending on is_continuous). Additional documentation from TransformedDistribution: Implements p(g(y)) / det|J(g(y))|, where g is the inverse of transform. Also raises a ValueError if inverse was not provided to the distribution and y was not returned from sample. Args: value: float or double Tensor. name: The name to give this op. Returns: prob: a Tensor of shape sample_shape(x) +

tf.contrib.distributions.TransformedDistribution.pmf(value, name='pmf') Probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.contrib.distributions.TransformedDistribution.pdf(value, name='pdf') Probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if not is_continuous.

tf.contrib.distributions.TransformedDistribution.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes. Args: sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample(). Returns: dict of parameter name to TensorShape. Raises: ValueError: if sample_shape is a TensorShape and is not fully defined.

tf.contrib.distributions.TransformedDistribution.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

tf.contrib.distributions.TransformedDistribution.parameters Dictionary of parameters used by this Distribution.

tf.contrib.distributions.TransformedDistribution.name Name prepended to all ops created by this Distribution.

tf.contrib.distributions.TransformedDistribution.mode(name='mode') Mode.