tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.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.InverseGammaWithSoftplusAlphaBeta.alpha Shape parameter.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.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 S

class tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta Inverse Gamma with softplus applied to alpha and beta.

tf.contrib.distributions.InverseGamma.__init__(alpha, beta, validate_args=False, allow_nan_stats=True, name='InverseGamma') Construct InverseGamma distributions with parameters alpha and beta. The parameters alpha and beta must be shaped in a way that supports broadcasting (e.g. alpha + beta is a valid operation). Args: alpha: Floating point tensor, the shape params of the distribution(s). alpha must contain only positive values. beta: Floating point tensor, the scale params of the distribut

tf.contrib.distributions.InverseGamma.variance(name='variance') Variance. Additional documentation from InverseGamma: Variance for inverse gamma is defined only for alpha > 2. If self.allow_nan_stats is False, an exception will be raised rather than returning NaN.

tf.contrib.distributions.InverseGamma.validate_args Python boolean indicated possibly expensive checks are enabled.

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.InverseGamma.std(name='std') Standard deviation.

tf.contrib.distributions.InverseGamma.sample_n(n, seed=None, name='sample_n') Generate n samples. Additional documentation from InverseGamma: See the documentation for tf.random_gamma for more details. Args: n: Scalar Tensor of type int32 or int64, the number of observations to sample. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with a prepended dimension (n,). Raises: TypeError: if n is not an integer type.