Difference between revisions of "MultiUniform"

Revision as of 02:05, 28 January 2016

MultiUniform(corr, I, J, lb, ub)

Full declaration:

MultiUniform(corr: Numeric[I, J]; I, J: IndexType; lb, ub: optional Numeric[I, J])

The multi-variate uniform distribution.

Generates vector samples (indexed by «I») such that each component has a uniform marginal distribution, and such that each component have the pair-wise correlations given by corr. Indexes «I» and «J» must have the same number of elements, corr needs to be symmetric and must obey a certain semidefinite condition (namely that the transformed matrix [2*sin(30*cov)] is positive semidefinite. In most cases, this roughly the same as corr being, or not being, positive semidefinite). «Lb» and «ub» can be used to specify upper and lower bounds, either for all components, or individually if these bounds are indexed by «I». If «lb» and «ub» are omitted, each component will have marginal Uniform(0, 1).

The correlation specified in corr is true sample correlation (Pearson correlation) - not rank correlation.

Library

Multivariate Distributions.ana

@@ Line 2: / Line 2: @@
 [[Category:Doc Status C]] <!-- For Lumina use, do not change -->
-= MultiUniform( corr, I,J, ''lb,ub'' ) =
+== MultiUniform(corr, I, J, ''lb, ub'') ==
 Full declaration:
- MultiUniform( corr : Numeric[I,J] ; I,J : IndexType ; lb,ub : optional Numeric[I,J] )
+:[[MultiUniform]](corr: Numeric[I, J]; I, J: IndexType; lb, ub: optional Numeric[I, J])
-The multi-variate uniform distribution.
+The multi-variate [[uniform]] distribution.
-Generates vector samples (indexed by I) such that each component has a uniform marginal distribution, and such that each component have the pair-wise correlations given by corr.  Indexes I and J must have the same number of elements, corr needs to be symmetric and must obey a certain semidefinite condition (namely that the transformed matrix [ 2*sin(30*cov) ] is positive semidefinite.  In most cases, this roughly the same as corr being, or not being, positive semidefinite).  Lb and ub can be used to specify upper and lower bounds, either for all components, or individually if these bounds are indexed by I.  If lb & ub are omitted, each component will have marginal [[Uniform]](0,1).
+Generates vector samples (indexed by «I») such that each component has a uniform marginal distribution, and such that each component have the pair-wise correlations given by corr.  Indexes «I» and «J» must have the same number of elements, corr needs to be symmetric and must obey a certain semidefinite condition (namely that the transformed matrix <code>[2*sin(30*cov)]</code> is positive semidefinite.  In most cases, this roughly the same as corr being, or not being, positive semidefinite). «Lb» and «ub» can be used to specify upper and lower bounds, either for all components, or individually if these bounds are indexed by «I».  If «lb» and «ub» are omitted, each component will have marginal [[Uniform]](0, 1).
 The correlation specified in corr is true sample [[Correlation|correlation]] (Pearson correlation) - not [[RankCorrel|rank correlation]].
-= Library =
+== Library ==
 Multivariate Distributions.ana
-= See Also =
+== See Also ==
 * [[Uniform]]
 * [[Gaussian]]
+* [[Multivariate distributions]]
+* [[Distribution Densities Library]]