tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.input_dict

tf.contrib.distributions.MultivariateNormalDiag.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.Bernoulli.std(name='std') Standard deviation.

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.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.Chi2.__init__(df, validate_args=False, allow_nan_stats=True, name='Chi2') Construct Chi2 distributions with parameter df. Args: df: Floating point tensor, the degrees of freedom of the distribution(s). df must contain only positive values. validate_args: Boolean, default False. Whether to assert that df > 0, and that x > 0 in the methods prob(x) and log_prob(x). If validate_args is False and the inputs are invalid, correct behavior is not guaranteed. allow_nan

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