tfd_log_cdf {tfprobability}

Log cumulative distribution function.

Description

Given random variable X, the cumulative distribution function cdf is: tfd_log_cdf(x) := Log[ P[X <= x] ] Often, a numerical approximation can be used for tfd_log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1.

Usage

tfd_log_cdf(distribution, value, ...)

Arguments

distribution

The distribution being used.

value

float or double Tensor.

...

Additional parameters passed to Python.

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_prob(), tfd_log_survival_function(), 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_cdf(x)