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

tf.contrib.distributions.InverseGamma.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.InverseGamma.beta Scale parameter.

tf.contrib.distributions.InverseGamma.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D Tensor. The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents. Args: name: name to give to the op Returns: batch_shape: Tensor.

tf.contrib.distributions.InverseGamma.alpha Shape parameter.

tf.contrib.distributions.InverseGamma.allow_nan_stats Python boolean describing behavior when a stat is undefined. Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1

class tf.contrib.distributions.InverseGamma The InverseGamma distribution with parameter alpha and beta. The parameters are the shape and inverse scale parameters alpha, beta. The PDF of this distribution is: pdf(x) = (beta^alpha)/Gamma(alpha)(x^(-alpha-1))e^(-beta/x), x > 0 and the CDF of this distribution is: cdf(x) = GammaInc(alpha, beta / x) / Gamma(alpha), x > 0 where GammaInc is the upper incomplete Gamma function. Examples: dist = InverseGamma(alpha=3.0, beta=2.0) dist2 = Inverse

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.__init__(alpha, beta, validate_args=False, allow_nan_stats=True, name='GammaWithSoftplusAlphaBeta')

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.variance(name='variance') Variance.