tf.contrib.distributions.QuantizedDistribution.log_survival_function()

tf.contrib.distributions.QuantizedDistribution.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.

Additional documentation from QuantizedDistribution:

For whole numbers y,

survival_function(y) := P[Y > y]
                      = 0, if y >= upper_cutoff,
                      = 1, 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 that fractional y are first floored to a whole number, and then above definition applies.

The base distribution's log_cdf method must be defined on y - 1.

Args:

• value: float or double Tensor.
• name: The name to give this op.

Returns:

Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.