- Extreme Optimization
- Documentation
- Statistics Library User's Guide
- Continuous Distributions
- Continuous Distributions
- The Beta Distribution
- The Cauchy Distribution
- The Chi Square Distribution
- The Erlang Distribution
- The Exponential Distribution
- The F Distribution
- The Gamma Distribution
- The Generalized Pareto Distribution
- The Gumbel Distribution
- The Laplace Distribution
- The Logistic Distribution
- Log-Logistic Distribution
- The Lognormal Distribution
- The Non-central Beta Distribution
- The Non-central Chi Square Distribution
- The Non-central F Distribution
- The Non-central Student t distribution
- The Normal Distribution
- The Pareto Distribution
- The Rayleigh Distribution
- Student's t Distribution
- The Transformed Beta Distribution
- The Transformed Gamma Distribution
- The Triangular Distribution
- The Continuous Uniform Distribution
- The Weibull Distribution

- The Rayleigh Distribution

The Rayleigh Distribution | Extreme Optimization Numerical Libraries for .NET Professional |

The Rayleigh distribution can be used to model the velocity of particles whose velocity in the x and y directions are independent and follow a normal distribution.

The Rayleigh distribution has a scale parameter. The probability density function is:

The Rayleigh distribution is a special case of the The Weibull Distribution.

The Rayleigh distribution is implemented by the RayleighDistribution class. It has one constructor that takes the scale parameter as its only argument.

The following constructs the Rayleigh distribution with scale parameter 1.8:

The RayleighDistribution class has two specific properties, LocationParameter and ScaleParameter, which return the location parameter (smallest possible value) and scale parameter of the distribution.

RayleighDistribution has one static (*Shared* in Visual Basic) method, Sample, which generates a
random sample using a user-supplied uniform random number generator. The second and third parameters are the
location and scale parameters of the distribution.

var random = new MersenneTwister(); double sample = RayleighDistribution.Sample(random, 1.8);

The above example uses the MersenneTwister class to generate uniform random numbers.

For details of the properties and methods common to all continuous distribution classes, see the topic on continuous distributions..

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