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java.lang.Objectumontreal.iro.lecuyer.probdistmulti.ContinuousDistributionMulti
umontreal.iro.lecuyer.probdistmulti.DirichletDist
public class DirichletDist
Implements the abstract class ContinuousDistributionMulti for the
 Dirichlet distribution with parameters
 (α1,...,αd), 
 αi > 0.
 The probability density is
 
 
| Constructor Summary | |
|---|---|
| DirichletDist(double[] alpha) | |
| Method Summary | |
|---|---|
|  double | density(double[] x). | 
| static double | density(double[] alpha,
        double[] x)Computes the density of the Dirichlet distribution with parameters (α1, ..., αd). | 
|  double[] | getAlpha()Returns the parameters (α1, ..., αd) of this object. | 
|  double | getAlpha(int i)Returns the ith component of the alpha vector. | 
|  double[][] | getCorrelation(). | 
| static double[][] | getCorrelation(double[] alpha)Computes the correlation matrix of the Dirichlet distribution with parameters (α1, ..., αd). | 
|  double[][] | getCovariance(). | 
| static double[][] | getCovariance(double[] alpha)Computes the covariance matrix of the Dirichlet distribution with parameters (α1, ..., αd). | 
|  double[] | getMean(). | 
| static double[] | getMean(double[] alpha)Computes the mean E[X] = αi/α0 of the Dirichlet distribution with parameters (α1, ..., αd), where α0 = ∑i=1dαi. | 
| static double[] | getMLE(double[][] x,
       int n,
       int d)Estimates the parameters [ hat(α_1),…, hat(α_d)] of the Dirichlet distribution using the maximum likelihood method. | 
|  void | setParams(double[] alpha)Sets the parameters (α1, ..., αd) of this object. | 
| Methods inherited from class umontreal.iro.lecuyer.probdistmulti.ContinuousDistributionMulti | 
|---|
| getDimension | 
| Methods inherited from class java.lang.Object | 
|---|
| equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait | 
| Constructor Detail | 
|---|
public DirichletDist(double[] alpha)
| Method Detail | 
|---|
public double density(double[] x)
ContinuousDistributionMulti![\begin{tabb}Returns $f(x_1, x_2, \ldots, x_d)$, the probability density of
 $X$\...
 ..._2, \ldots, x_d\}$. The convention is that
 $\texttt{x[i-1]} = x_i$.
 \end{tabb}](ContinuousDistributionMultiimg1.png) 
 
density in class ContinuousDistributionMultipublic double[] getMean()
ContinuousDistributionMulti![\begin{tabb}
 Returns the mean vector of the distribution, defined as $\mu_{i} = E[X_i]$.
 \end{tabb}](ContinuousDistributionMultiimg3.png) 
getMean in class ContinuousDistributionMultipublic double[][] getCovariance()
ContinuousDistributionMulti![\begin{tabb}
 Returns the variance-covariance matrix of the distribution, defined as\\
 $\sigma_{ij} = E[(X_i - \mu_i)(X_j - \mu_j)]$.
 \end{tabb}](ContinuousDistributionMultiimg4.png) 
getCovariance in class ContinuousDistributionMultipublic double[][] getCorrelation()
ContinuousDistributionMulti 
getCorrelation in class ContinuousDistributionMulti
public static double density(double[] alpha,
                             double[] x)
public static double[][] getCovariance(double[] alpha)
public static double[][] getCorrelation(double[] alpha)
public static double[] getMLE(double[][] x,
                              int n,
                              int d)
x - the list of observations to use to evaluate parametersn - the number of observations to use to evaluate parametersd - the dimension of each vector
public static double[] getMean(double[] alpha)
public double[] getAlpha()
public double getAlpha(int i)
public void setParams(double[] alpha)
| 
 | SSJ V. labo. | ||||||||
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