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MultivariateEmpiricalDistribution Methods |
The MultivariateEmpiricalDistribution type exposes the following members.
Methods| Name | Description | |
|---|---|---|
 | Clone |
Creates a new object that is a copy of the current instance.
(Overrides DistributionBaseClone.) |
| ComplementaryDistributionFunction |
Gets the complementary cumulative distribution function
(ccdf) for this distribution evaluated at point x.
This function is also known as the Survival function.
(Inherited from MultivariateContinuousDistribution.) |
| DistributionFunction |
Gets the probability density function (pdf) for
this distribution evaluated at point x.
(Inherited from MultivariateContinuousDistribution.) |
| Equals | Determines whether the specified object is equal to the current object. (Inherited from Object.) |
 | Finalize | Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.) |
| Fit(Double) |
Fits the underlying distribution to a given set of observations.
(Inherited from MultivariateContinuousDistribution.) |
| Fit(Double, IFittingOptions) |
Fits the underlying distribution to a given set of observations.
(Inherited from MultivariateContinuousDistribution.) |
| Fit(Double, Double) |
Fits the underlying distribution to a given set of observations.
(Inherited from MultivariateContinuousDistribution.) |
| Fit(Double, Int32) |
Fits the underlying distribution to a given set of observations.
(Inherited from MultivariateContinuousDistribution.) |
| Fit(Double, Double, IFittingOptions) |
Fits the underlying distribution to a given set of observations.
(Overrides MultivariateContinuousDistributionFit(Double, Double, IFittingOptions).) |
| Fit(Double, Double, MultivariateEmpiricalOptions) |
Fits the underlying distribution to a given set of observations.
|
| Fit(Double, Int32, IFittingOptions) |
Fits the underlying distribution to a given set of observations.
(Overrides MultivariateContinuousDistributionFit(Double, Int32, IFittingOptions).) |
| Fit(Double, Int32, MultivariateEmpiricalOptions) |
Fits the underlying distribution to a given set of observations.
|
| Generate |
Generates a random observation from the current distribution.
(Inherited from MultivariateContinuousDistribution.) |
| Generate(Double) |
Generates a random observation from the current distribution.
(Inherited from MultivariateContinuousDistribution.) |
| Generate(Int32) |
Generates a random vector of observations from the current distribution.
(Inherited from MultivariateContinuousDistribution.) |
| Generate(Int32) |
Generates a random observation from the current distribution.
(Inherited from MultivariateContinuousDistribution.) |
| Generate(Random) |
Generates a random observation from the current distribution.
(Inherited from MultivariateContinuousDistribution.) |
| Generate(Double, Random) |
Generates a random observation from the current distribution.
(Inherited from MultivariateContinuousDistribution.) |
| Generate(Int32, Double) |
Generates a random vector of observations from the current distribution.
(Inherited from MultivariateContinuousDistribution.) |
| Generate(Int32, Int32) |
Generates a random vector of observations from the current distribution.
(Inherited from MultivariateContinuousDistribution.) |
| Generate(Int32, Random) |
Generates a random vector of observations from the current distribution.
(Inherited from MultivariateContinuousDistribution.) |
| Generate(Int32, Random) |
Generates a random observation from the current distribution.
(Inherited from MultivariateContinuousDistribution.) |
| Generate(Int32, Int32, Random) |
Generates a random vector of observations from the current distribution.
(Inherited from MultivariateContinuousDistribution.) |
| Generate(Int32, Double, Random) |
Generates a random vector of observations from the current distribution.
(Overrides MultivariateContinuousDistributionGenerate(Int32, Double, Random).) |
| GetHashCode | Serves as the default hash function. (Inherited from Object.) |
| GetType | Gets the Type of the current instance. (Inherited from Object.) |
| InnerComplementaryDistributionFunction |
Gets the complementary cumulative distribution function
(ccdf) for this distribution evaluated at point x.
This function is also known as the Survival function.
(Inherited from MultivariateContinuousDistribution.) |
| InnerDistributionFunction |
Gets the probability density function (pdf) for
this distribution evaluated at point x.
(Overrides MultivariateContinuousDistributionInnerDistributionFunction(Double).) |
| InnerLogProbabilityDensityFunction |
Gets the log-probability density function (pdf)
for this distribution evaluated at point x.
(Inherited from MultivariateContinuousDistribution.) |
| InnerProbabilityDensityFunction |
Gets the probability density function (pdf) for
this distribution evaluated at point x.
(Overrides MultivariateContinuousDistributionInnerProbabilityDensityFunction(Double).) |
| LogProbabilityDensityFunction |
Gets the log-probability density function (pdf)
for this distribution evaluated at point x.
(Inherited from MultivariateContinuousDistribution.) |
| MemberwiseClone | Creates a shallow copy of the current Object. (Inherited from Object.) |
| ProbabilityDensityFunction |
Gets the probability density function (pdf) for
this distribution evaluated at point x.
(Inherited from MultivariateContinuousDistribution.) |
 | SilvermanRule(Double) |
Gets the Silverman's rule. estimative of the smoothing parameter.
This is the default smoothing rule applied used when estimating
MultivariateEmpiricalDistributions.
|
| SilvermanRule(Double, Double) |
Gets the Silverman's rule. estimative of the smoothing parameter.
This is the default smoothing rule applied used when estimating
MultivariateEmpiricalDistributions.
|
| SilvermanRule(Double, Int32) |
Gets the Silverman's rule. estimative of the smoothing parameter.
This is the default smoothing rule applied used when estimating
MultivariateEmpiricalDistributions.
|
| SilvermanRule(Double, Double, Int32) |
Gets the Silverman's rule. estimative of the smoothing parameter.
This is the default smoothing rule applied used when estimating
MultivariateEmpiricalDistributions.
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| ToString |
Returns a String that represents this instance.
(Inherited from DistributionBase.) |
| ToString(IFormatProvider) |
Returns a String that represents this instance.
(Inherited from DistributionBase.) |
| ToString(String) |
Returns a String that represents this instance.
(Inherited from DistributionBase.) |
| ToString(String, IFormatProvider) |
Returns a String that represents this instance.
(Overrides DistributionBaseToString(String, IFormatProvider).) |
Extension Methods| Name | Description | |
|---|---|---|
 | HasMethod |
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.) |
| IsEqual |
Compares two objects for equality, performing an elementwise
comparison if the elements are vectors or matrices.
(Defined by Matrix.) |
| To(Type) | Overloaded.
Converts an object into another type, irrespective of whether
the conversion can be done at compile time or not. This can be
used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.) |
| ToT | Overloaded.
Converts an object into another type, irrespective of whether
the conversion can be done at compile time or not. This can be
used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.) |
See Also