Classes implementing continuous multi-dimensional distributions should inherit from this class. More...
Public Member Functions | |
| abstract double | density (double[] x) |
| Returns \(f(x_1, x_2, …, x_d)\), the probability density of \(X\) evaluated at the point \(x\), where \(x = \{x_1, x_2, …, x_d\}\). | |
| int | getDimension () |
| Returns the dimension \(d\) of the distribution. | |
| abstract double[] | getMean () |
| Returns the mean vector of the distribution, defined as \(\mu_i = E[X_i]\). | |
| abstract double[][] | getCovariance () |
| Returns the variance-covariance matrix of the distribution, defined as \(\sigma_{ij} = E[(X_i - \mu_i)(X_j - \mu_j)]\). | |
| abstract double[][] | getCorrelation () |
| Returns the correlation matrix of the distribution, defined as. | |
Detailed Description
Classes implementing continuous multi-dimensional distributions should inherit from this class.
Such distributions are characterized by a density function \(f(x_1, x_2, …, x_d)\); thus the signature of a density method is supplied here. All array indices start at 0.
Definition at line 40 of file ContinuousDistributionMulti.java.
Member Function Documentation
◆ density()
|
abstract |
Returns \(f(x_1, x_2, …, x_d)\), the probability density of \(X\) evaluated at the point \(x\), where \(x = \{x_1, x_2, …, x_d\}\).
The convention is that \(\mathtt{x[i-1]} = x_i\).
- Parameters
-
x value at which the density is evaluated
- Returns
- density function evaluated at x
Reimplemented in umontreal.ssj.probdistmulti.ContinuousDistribution2Dim, umontreal.ssj.probdistmulti.DirichletDist, and umontreal.ssj.probdistmulti.MultiNormalDist.
◆ getCorrelation()
|
abstract |
Returns the correlation matrix of the distribution, defined as.
\(\rho_{ij} = \sigma_{ij}/\sqrt{\sigma_{ii}\sigma_{jj}}\).
Reimplemented in umontreal.ssj.probdistmulti.BiNormalDist, umontreal.ssj.probdistmulti.BiStudentDist, umontreal.ssj.probdistmulti.DirichletDist, and umontreal.ssj.probdistmulti.MultiNormalDist.
◆ getCovariance()
|
abstract |
Returns the variance-covariance matrix of the distribution, defined as
\(\sigma_{ij} = E[(X_i - \mu_i)(X_j - \mu_j)]\).
Reimplemented in umontreal.ssj.probdistmulti.BiNormalDist, umontreal.ssj.probdistmulti.BiStudentDist, umontreal.ssj.probdistmulti.DirichletDist, and umontreal.ssj.probdistmulti.MultiNormalDist.
◆ getDimension()
| int umontreal.ssj.probdistmulti.ContinuousDistributionMulti.getDimension | ( | ) |
Returns the dimension \(d\) of the distribution.
Reimplemented in umontreal.ssj.probdistmulti.MultiNormalDist.
Definition at line 56 of file ContinuousDistributionMulti.java.
◆ getMean()
|
abstract |
Returns the mean vector of the distribution, defined as \(\mu_i = E[X_i]\).
Reimplemented in umontreal.ssj.probdistmulti.BiNormalDist, umontreal.ssj.probdistmulti.BiStudentDist, umontreal.ssj.probdistmulti.DirichletDist, and umontreal.ssj.probdistmulti.MultiNormalDist.
The documentation for this class was generated from the following file:
- src/main/java/umontreal/ssj/probdistmulti/ContinuousDistributionMulti.java
