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Independent Class |
Joint distribution assuming independence between vector components.
Inheritance HierarchySystem.Object
Accord.Statistics.Distributions.DistributionBase
Accord.Statistics.Distributions.Multivariate.MultivariateContinuousDistribution
Accord.Statistics.Distributions.Multivariate.Independent<IUnivariateDistribution>
Accord.Statistics.Distributions.Multivariate.Independent
Accord.Statistics.Distributions.DistributionBase
Accord.Statistics.Distributions.Multivariate.MultivariateContinuousDistribution
Accord.Statistics.Distributions.Multivariate.Independent<IUnivariateDistribution>
Accord.Statistics.Distributions.Multivariate.Independent
Namespace: Accord.Statistics.Distributions.Multivariate
Assembly: Accord.Statistics (in Accord.Statistics.dll) Version: 3.8.0
SyntaxThe Independent type exposes the following members.
Constructors| Name | Description | |
|---|---|---|
 | Independent |
Initializes a new instance of the Independent< TDistribution> class.
|
Properties| Name | Description | |
|---|---|---|
 | Components |
Gets the components of this joint distribution.
(Inherited from Independent<TDistribution>.) |
| Covariance |
Gets the variance-covariance matrix for this distribution.
(Inherited from Independent<TDistribution>.) |
| Dimension |
Gets the number of variables for this distribution.
(Inherited from MultivariateContinuousDistribution.) |
| Item |
Gets or sets the components of this joint distribution.
(Inherited from Independent<TDistribution>.) |
| Mean |
Gets the mean for this distribution.
(Inherited from Independent<TDistribution>.) |
| Median |
Gets the median for this distribution.
(Inherited from MultivariateContinuousDistribution.) |
| Mode |
Gets the mode for this distribution.
(Inherited from MultivariateContinuousDistribution.) |
| Variance |
Gets the variance for this distribution.
(Inherited from Independent<TDistribution>.) |
Methods| Name | Description | |
|---|---|---|
| Clone |
Creates a new object that is a copy of the current instance.
(Overrides Independent<TDistribution>.Clone().) |
| 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.
(Inherited from Independent<TDistribution>.) |
| Fit(Double[][],Double[], IndependentOptions) |
Fits the underlying distribution to a given set of observations.
(Inherited from Independent<TDistribution>.) |
| Fit(Double[][],Int32[], IFittingOptions) |
Fits the underlying distribution to a given set of observations.
(Inherited from MultivariateContinuousDistribution.) |
| 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,Double[][], Random) |
Generates a random vector of observations from the current distribution.
(Inherited from Independent<TDistribution>.) |
| Generate(Int32,Int32[][], Random) |
Generates a random vector of observations from the current distribution.
(Inherited from MultivariateContinuousDistribution.) |
| 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.
(Inherited from Independent<TDistribution>.) |
| InnerLogProbabilityDensityFunction |
Gets the log-probability density function (pdf)
for this distribution evaluated at point x.
(Inherited from Independent<TDistribution>.) |
| InnerProbabilityDensityFunction |
Gets the probability density function (pdf) for
this distribution evaluated at point x.
(Inherited from Independent<TDistribution>.) |
| 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.) |
| Reset |
Resets cached values (should be called after re-estimation).
(Inherited from Independent<TDistribution>.) |
| 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.
(Inherited from Independent<TDistribution>.) |
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.) |
| To<T>() | 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.) |
RemarksIn probability and statistics, given at least two random variables X, Y, ..., that are defined on a probability space, the joint probability distribution for X, Y, ... is a probability distribution that gives the probability that each of X, Y, ... falls in any particular range or discrete set of values specified for that variable. In the case of only two random variables, this is called a bivariate distribution, but the concept generalizes to any number of random variables, giving a multivariate distribution.
This class is also available in a generic version, allowing for any choice of component distribution (Independent<TDistribution>.
References:
See Also