Module rand::distributions [] [src]

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.