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

tf.contrib.distributions.QuantizedDistribution.cdf(value, name='cdf') Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x] Additional documentation from QuantizedDistribution: For whole numbers y, cdf(y) := P[Y <= y] = 1, if y >= upper_cutoff, = 0, if y < lower_cutoff, = P[X <= y], otherwise. Since Y only has mass at whole numbers, P[Y <= y] = P[Y <= floor(y)]. This dictates th

tf.contrib.distributions.QuantizedDistribution.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.QuantizedDistribution.base_distribution Base distribution, p(x).

tf.contrib.distributions.QuantizedDistribution.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 f

class tf.contrib.distributions.QuantizedDistribution Distribution representing the quantization Y = ceiling(X).

tf.contrib.distributions.Poisson.__init__(lam, validate_args=False, allow_nan_stats=True, name='Poisson') Construct Poisson distributions. Args: lam: Floating point tensor, the rate parameter of the distribution(s). lam must be positive. validate_args: Boolean, default False. Whether to assert that lam > 0 as well as inputs to pmf computations are non-negative integers. If validate_args is False, then pmf computations might return NaN, but can be evaluated at any real value. allow_nan_st

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

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

tf.contrib.distributions.Poisson.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`.