tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.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.Gamma.beta Inverse scale parameter.

tf.contrib.bayesflow.stochastic_tensor.NormalTensor.mean(name='mean')

tf.contrib.distributions.MultivariateNormalCholesky.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.Mixture.num_components

tf.contrib.distributions.Dirichlet.mode(name='mode') Mode. Additional documentation from Dirichlet: Note that the mode for the Dirichlet distribution is only defined when alpha > 1. This returns the mode when alpha > 1, and NaN otherwise. If self.allow_nan_stats is False, an exception will be raised rather than returning NaN.

tf.contrib.rnn.GRUBlockCell.__call__(x, h_prev, scope=None) GRU cell.

tf.contrib.distributions.Multinomial.dtype The DType of Tensors handled by this Distribution.

tf.contrib.distributions.Poisson.sample_n(n, seed=None, name='sample_n') Generate n samples. 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 type.

tf.contrib.distributions.BetaWithSoftplusAB.get_event_shape() Shape of a single sample from a single batch as a TensorShape. Same meaning as event_shape. May be only partially defined. Returns: event_shape: TensorShape, possibly unknown.