Variance.

Variance is defined as, Var = E[(X - E[X])**2] where X is the random variable associated with this distribution, E denotes expectation, and Var$shape = batch_shape + event_shape.

tfd_variance(distribution, ...)

Arguments

distribution	The distribution being used.
...	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_cdf(), tfd_log_prob(), tfd_log_survival_function(), tfd_mean(), tfd_mode(), tfd_prob(), tfd_quantile(), tfd_sample(), tfd_stddev(), tfd_survival_function()

Examples

# \donttest{
  d <- tfd_normal(loc = c(1, 2), scale = c(1, 0.5))
  d %>% tfd_variance()
#> tf.Tensor([1.   0.25], shape=(2,), dtype=float32)
# }