WaldDistribution Class |
Inheritance HierarchyMeta.Numerics.Statistics.DistributionsUnivariateDistribution
Meta.Numerics.Statistics.DistributionsContinuousDistribution
Meta.Numerics.Statistics.DistributionsWaldDistribution
Namespace: Meta.Numerics.Statistics.Distributions
Assembly: Meta.Numerics (in Meta.Numerics.dll) Version: 4.1.4
SyntaxThe WaldDistribution type exposes the following members.
Constructors| Name | Description | |
|---|---|---|
 | WaldDistribution |
Initializes a new Wald distribution.
|
Properties| Name | Description | |
|---|---|---|
 | ExcessKurtosis |
Gets the excess kurtosis of the distribution.
(Overrides UnivariateDistributionExcessKurtosis.) |
| Mean |
Gets the mean of the distribution.
(Overrides UnivariateDistributionMean.) |
| Median |
Gets the median of the distribution.
(Inherited from ContinuousDistribution.) |
| Shape |
Gets the shape parameter of the distribution.
|
| Skewness |
Gets the skewness of the distribution.
(Overrides UnivariateDistributionSkewness.) |
| StandardDeviation |
Gets the standard deviation of the distribution.
(Inherited from UnivariateDistribution.) |
| Support |
Gets the interval over which the distribution is non-vanishing.
(Overrides ContinuousDistributionSupport.) |
| Variance |
Gets the variance of the distribution.
(Overrides UnivariateDistributionVariance.) |
Methods| Name | Description | |
|---|---|---|
| CentralMoment |
Computes a central moment of the distribution.
(Overrides ContinuousDistributionCentralMoment(Int32).) |
| Cumulant |
Computes a cumulant of the distribution.
(Overrides UnivariateDistributionCumulant(Int32).) |
| Equals | Determines whether the specified object is equal to the current object. (Inherited from Object.) |
| ExpectationValue |
Computes the expectation value of the given function.
(Inherited from ContinuousDistribution.) |
 | FitToSample |
Determines the parameters of the Wald distribution that best fits a sample.
|
| GetHashCode | Serves as the default hash function. (Inherited from Object.) |
| GetRandomValue |
Generates a random variate.
(Overrides ContinuousDistributionGetRandomValue(Random).) |
| GetRandomValues |
Generates the given number of random variates.
(Inherited from ContinuousDistribution.) |
| GetType | Gets the Type of the current instance. (Inherited from Object.) |
| Hazard |
Computes the hazard function.
(Inherited from ContinuousDistribution.) |
| InverseLeftProbability |
Returns the point at which the cumulative distribution function attains a given value.
(Inherited from ContinuousDistribution.) |
| InverseRightProbability |
Returns the point at which the right probability function attains the given value.
(Inherited from ContinuousDistribution.) |
| LeftProbability |
Returns the cumulative probability to the left of (below) the given point.
(Overrides ContinuousDistributionLeftProbability(Double).) |
| ProbabilityDensity |
Returns the probability density at the given point.
(Overrides ContinuousDistributionProbabilityDensity(Double).) |
| RawMoment |
Computes a raw moment of the distribution.
(Overrides ContinuousDistributionRawMoment(Int32).) |
| RightProbability |
Returns the cumulative probability to the right of (above) the given point.
(Overrides ContinuousDistributionRightProbability(Double).) |
| ToString | Returns a string that represents the current object. (Inherited from Object.) |
RemarksThe Wald distribution, also called the inverse Gaussian distribution, is the distribution of first passage times for Brownian motion.
In Brownian motion, a particle moves randomly so that its position at any given time is distributed normally with a mean that increases linearly and a standard deviation that increases with the square root of time. The first passage time is the earliest time that its position reaches a given level. This first passage time is Wald distributed with mean and shape parameters related to the drift, noise, and threshold.
This may appear a very obscure an technical relationship, but it turns out to have myriad applications: to stock prices, ballot counting, neurological response times, and earthquake prediction.
See Also