Normal Class |
Continuous Univariate Normal distribution, also known as Gaussian distribution.
For details about this distribution, see
Wikipedia - Normal distribution.
Inheritance HierarchyNamespace: Altaxo.Calc.Distributions
Assembly: AltaxoCore (in AltaxoCore.dll) Version: 4.8.3179.0 (4.8.3179.0)
SyntaxC#
public class Normal : IContinuousDistribution, IUnivariateDistribution, IDistribution
The Normal type exposes the following members.
Constructors| Name | Description | |
|---|---|---|
 | Normal | Initializes a new instance of the Normal class. This is a normal distribution with mean 0.0 and standard deviation 1.0. The distribution will be initialized with the default random number generator. |
| Normal(Random) | Initializes a new instance of the Normal class. This is a normal distribution with mean 0.0 and standard deviation 1.0. The distribution will be initialized with the default random number generator. |
| Normal(Double, Double) | Initializes a new instance of the Normal class with a particular mean and standard deviation. The distribution will be initialized with the default random number generator. |
| Normal(Double, Double, Random) | Initializes a new instance of the Normal class with a particular mean and standard deviation. The distribution will be initialized with the default random number generator. |
Properties| Name | Description | |
|---|---|---|
 | Entropy | Gets the entropy of the normal distribution. |
| Maximum | Gets the maximum of the normal distribution. |
| Mean | Gets the mean (μ) of the normal distribution. |
| Median | Gets the median of the normal distribution. |
| Minimum | Gets the minimum of the normal distribution. |
| Mode | Gets the mode of the normal distribution. |
| Precision | Gets the precision of the normal distribution. |
| RandomSource | Gets the random number generator which is used to draw random samples. |
| Skewness | Gets the skewness of the normal distribution. |
| StdDev | Gets the standard deviation (σ) of the normal distribution. Range: σ ≥ 0. |
| Variance | Gets the variance of the normal distribution. |
Methods| Name | Description | |
|---|---|---|
 | CDF | Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x). |
| CumulativeDistribution | Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x). |
| Density | Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x. |
| DensityLn | Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x). |
| Equals | Determines whether the specified object is equal to the current object. (Inherited from Object) |
| Estimate | Estimates the normal distribution parameters from sample data with maximum-likelihood. |
 | Finalize | Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object) |
| GetHashCode | Serves as the default hash function. (Inherited from Object) |
| GetType | Gets the Type of the current instance. (Inherited from Object) |
| InvCDF | Computes the inverse of the cumulative distribution function (InvCDF) for the distribution at the given probability. This is also known as the quantile or percent point function. |
| InverseCumulativeDistribution | Computes the inverse of the cumulative distribution function (InvCDF) for the distribution at the given probability. This is also known as the quantile or percent point function. |
| IsValidParameterSet | Tests whether the provided values are valid parameters for this distribution. |
| MemberwiseClone | Creates a shallow copy of the current Object. (Inherited from Object) |
| Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x. | |
| PDFLn | Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x). |
| Sample | Generates a sample from the normal distribution using the Box-Muller algorithm. |
| Sample(Double, Double) | Generates a sample from the normal distribution using the Box-Muller algorithm. |
| Sample(Random, Double, Double) | Generates a sample from the normal distribution using the Box-Muller algorithm. |
| Samples | Generates a sequence of samples from the normal distribution using the Box-Muller algorithm. |
| Samples(Double) | Fills an array with samples generated from the distribution. |
| Samples(Double, Double) | Generates a sequence of samples from the normal distribution using the Box-Muller algorithm. |
| Samples(Double, Double, Double) | Fills an array with samples generated from the distribution. |
| Samples(Random, Double, Double) | Generates a sequence of samples from the normal distribution using the Box-Muller algorithm. |
| Samples(Random, Double, Double, Double) | Fills an array with samples generated from the distribution. |
| ToString |
A string representation of the distribution.
(Overrides ObjectToString) |
| WithMeanPrecision | Constructs a normal distribution from a mean and precision. |
| WithMeanStdDev | Constructs a normal distribution from a mean and standard deviation. |
| WithMeanVariance | Constructs a normal distribution from a mean and variance. |
See Also