Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x]

Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x]

tfd_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_covariance(), tfd_cross_entropy(), tfd_entropy(), tfd_kl_divergence(), tfd_log_cdf(), 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

# \donttest{
  d <- tfd_normal(loc = c(1, 2), scale = c(1, 0.5))
  x <- d %>% tfd_sample()
  d %>% tfd_cdf(x)
#> tf.Tensor([0.23162228 0.02920183], shape=(2,), dtype=float32)
# }