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

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

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.mu Locations of these Student's t distribution(s).

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

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.mean(name='mean') Mean. Additional documentation from StudentT: The mean of Student's T equals mu if df > 1, otherwise it is NaN. If self.allow_nan_stats=True, then an exception will be raised rather than returning NaN.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.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: val

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.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.