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.
tfd_log_cdf(distribution, value, ...)
| distribution | The distribution being used. |
|---|---|
| value | float or double Tensor. |
| ... | Additional parameters passed to Python. |
a Tensor of shape sample_shape(x) + self$batch_shape with values of type self$dtype.
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()
# \donttest{ d <- tfd_normal(loc = c(1, 2), scale = c(1, 0.5)) x <- d %>% tfd_sample() d %>% tfd_log_cdf(x)#> tf.Tensor([-0.05474529 -1.1571263 ], shape=(2,), dtype=float32)# }