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

tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.value_type

tf.contrib.bayesflow.stochastic_tensor.GammaTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.clone(name=None, **dist_args)

class tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor TransformedDistributionTensor is a StochasticTensor backed by the distribution TransformedDistribution.

class tf.contrib.distributions.DirichletMultinomial DirichletMultinomial mixture distribution. This distribution is parameterized by a vector alpha of concentration parameters for k classes and n, the counts per each class..

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

tf.errors.CancelledError.__init__(node_def, op, message) Creates a CancelledError.

tf.contrib.distributions.TransformedDistribution.log_cdf(value, name='log_cdf') Log cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: log_cdf(x) := Log[ P[X <= x] ] Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1. Args: value: float or double Tensor. name: The name to give this op. Returns: logcdf: a Tensor of shape sample_

tf.contrib.distributions.Uniform.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.