NormalDistribution.Sample Method

public override double Sample(
	Random random
)

Public Overrides Function Sample ( 
	random As Random
) As Double

public:
virtual double Sample(
	Random^ random
) override

abstract Sample : 
        random : Random -> float 
override Sample : 
        random : Random -> float 

Parameters

random  Random

The Random derived random number generator used to generate the sample.

Return Value

This method uses the random number generator specified by random to generate a random sample from the distribution. The return values of successive calls to this method follow the distribution represented by this distribution.

public static double Sample(
	Random random,
	double mean
)

Public Shared Function Sample ( 
	random As Random,
	mean As Double
) As Double

public:
static double Sample(
	Random^ random, 
	double mean
)

static member Sample : 
        random : Random * 
        mean : float -> float 

Parameters

random  Random

The Random derived random number generator used to generate the sample.

mean  Double

The mean of the distribution.

Return Value

This method is useful when only a single random sample is required, or if the parameters of the distribution change often. To obtain a large number of samples from a distribution with identical parameters, create an instance of the class and call the Sample() method repeatedly.

public static double Sample(
	Random random,
	double mean,
	double standardDeviation
)

Public Shared Function Sample ( 
	random As Random,
	mean As Double,
	standardDeviation As Double
) As Double

public:
static double Sample(
	Random^ random, 
	double mean, 
	double standardDeviation
)

static member Sample : 
        random : Random * 
        mean : float * 
        standardDeviation : float -> float 

Parameters

random  Random

The Random derived random number generator used to generate the sample.

mean  Double

The mean of the distribution.

standardDeviation  Double

The standard deviation of the distribution.

Return Value

This method is useful when only a single random sample is required, or if the parameters of the distribution change often. To obtain a large number of samples from a distribution with identical parameters, create an instance of the class and call the Sample() method repeatedly.

Reference