tf.contrib.distributions.Chi2WithAbsDf.cdf(value, name='cdf') Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x] Args: value: float or double Tensor. name: The name to give this op. Returns: cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.LaplaceWithSoftplusScale.get_batch_shape() Shape of a single sample from a single event index as a TensorShape. Same meaning as batch_shape. May be only partially defined. Returns: batch_shape: TensorShape, possibly unknown.

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

tf.contrib.distributions.Chi2WithAbsDf.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). 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.

tf.contrib.distributions.InverseGamma.survival_function(value, name='survival_function') Survival function. Given random variable X, the survival function is defined: survival_function(x) = P[X > x] = 1 - P[X <= x] = 1 - cdf(x). Args: value: float or double Tensor. name: The name to give this op. Returns: Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.

tf.contrib.distributions.Chi2WithAbsDf.log_survival_function(value, name='log_survival_function') Log survival function. Given random variable X, the survival function is defined: log_survival_function(x) = Log[ P[X > x] ] = Log[ 1 - P[X <= x] ] = Log[ 1 - cdf(x) ] Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1. Args: value: float or doub

void tensorflow::PartialTensorShape::AsProto(TensorShapeProto *proto) const Fill *proto from *this.

tf.contrib.graph_editor.reroute_a2b_ts(ts0, ts1, can_modify=None, cannot_modify=None) For each tensor's pair, replace the end of t1 by the end of t0. B0 B1 B0 B1 | | => |/ A0 A1 A0 A1 The end of the tensors in ts1 are left dangling. Args: ts0: an object convertible to a list of tf.Tensor. ts1: an object convertible to a list of tf.Tensor. can_modify: iterable of operations which can be modified. Any operation outside within_ops will be left untouched by this function. cannot_modify: ite

tf.contrib.distributions.Distribution.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.contrib.distributions.StudentT.entropy(name='entropy') Shanon entropy in nats.