ContinuousDistribution

Common interface for continuous probability distributions.

A continuous distribution assigns probabilities to intervals of real numbers via a probability density function (PDF). Implementations provide methods to evaluate the density, log-density, cumulative probability, quantiles, and random sampling.

Extends Distribution, which provides shared statistical properties such as mean, variance, standardDeviation, skewness, kurtosis, and the sf survival function.

Example:

val dist: ContinuousDistribution = NormalDistribution(mu = 0.0, sigma = 1.0)
dist.pdf(0.0)              // 0.3989... (peak density at the mean)
dist.logPdf(0.0)           // -0.9189... (log of the density)
dist.cdf(1.96)             // 0.975... (area under the curve up to 1.96)
dist.quantile(0.975)       // 1.96 (inverse of cdf)
dist.sample(Random(42))    // a single random draw
dist.sample(5, Random(42)) // five random draws

See also

Inheritors