Log cumulative distribution function.
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()
d <- tfd_normal(loc = c(1, 2), scale = c(1, 0.5)) x <- d %>% tfd_sample() d %>% tfd_log_cdf(x)
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