tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.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 sha

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.is_reparameterized

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.is_continuous

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.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.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.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.distributions.InverseGammaWithSoftplusAlphaBeta.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32 Tensor. Args: name: name to give to the op Returns: event_shape: Tensor.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.entropy(name='entropy') Shanon entropy in nats. Additional documentation from InverseGamma: This is defined to be entropy = alpha - log(beta) + log(Gamma(alpha)) + (1-alpha)digamma(alpha) where digamma(alpha) is the digamma function.

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

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.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.InverseGammaWithSoftplusAlphaBeta.beta Scale parameter.