tfd_log_survival_function: Log survival function.

In tfprobability: Interface to 'TensorFlow Probability'

Log survival function.

Description

Given random variable X, the survival function is defined: tfd_log_survival_function(x) = Log[ P[X > x] ] = Log[ 1 - P[X <= x] ] = Log[ 1 - cdf(x) ]

Usage

tfd_log_survival_function(distribution, value, ...)

Arguments

distribution	The distribution being used.
value	float or double Tensor.
...	Additional parameters passed to Python.

Details

Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1.

Value

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

See Also

Other distribution_methods: tfd_cdf(), tfd_covariance(), tfd_cross_entropy(), tfd_entropy(), tfd_kl_divergence(), tfd_log_cdf(), tfd_log_prob(), tfd_mean(), tfd_mode(), tfd_prob(), tfd_quantile(), tfd_sample(), tfd_stddev(), tfd_survival_function(), tfd_variance()

Examples

  d <- tfd_normal(loc = c(1, 2), scale = c(1, 0.5))
  x <- d %>% tfd_sample()
  d %>% tfd_log_survival_function(x)

tfprobability documentation built on Sept. 1, 2022, 5:07 p.m.