Module rand::distributions

Sampling from random distributions.

This is a generalization of Rand to allow parameters to control the exact properties of the generated values, e.g. the mean and standard deviation of a normal distribution. The Sample trait is the most general, and allows for generating values that change some state internally. The IndependentSample trait is for generating values that do not need to record state.

Modules

exponential	The exponential distribution.
gamma	The Gamma and derived distributions.
normal	The normal and derived distributions.
range	Generating numbers between two others.

Structs

ChiSquared	The chi-squared distribution χ²(k), where k is the degrees of freedom.
Exp	The exponential distribution Exp(lambda).
FisherF	The Fisher F distribution F(m, n).
Gamma	The Gamma distribution Gamma(shape, scale) distribution.
LogNormal	The log-normal distribution ln N(mean, std_dev**2).
Normal	The normal distribution N(mean, std_dev**2).
RandSample	A wrapper for generating types that implement Rand via the Sample & IndependentSample traits.
Range	Sample values uniformly between two bounds.
StudentT	The Student t distribution, t(nu), where nu is the degrees of freedom.
Weighted	A value with a particular weight for use with WeightedChoice.
WeightedChoice	A distribution that selects from a finite collection of weighted items.

Traits

IndependentSample	Samples that do not require keeping track of state.
Sample	Types that can be used to create a random instance of Support.