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git-svn-id: https://www.dynare.org/svn/dynare/dynare_v4@1615 ac1d8469-bf42-47a9-8791-bf33cf982152
time-shift
assia 2008-01-17 16:16:32 +00:00
parent a9cb80a29e
commit d8135e533a
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function G = rand_inverse_wishart(m, v, H_inv_upper_chol)
% function G = rand_inverse_wishart(m, v, H_inv_upper_chol)
% rand_inverse_wishart Pseudo random matrices drawn from an
% inverse Wishart distribution
%
% inverse Wishart distribution
% G = rand_inverse_wishart(m, v, H_inv_upper_chol)
%
% Returns an m-by-m matrix drawn from an inverse-Wishart distribution.
%
% m: dimension of G and H_inv_upper_chol.
% v: degrees of freedom, greater or equal than m.
% H_inv_chol: upper cholesky decomposition of the inverse of the
% matrix parameter.
% The upper cholesky of the inverse is requested here
% in order to avoid to recompute it at every random draw.
% H_inv_upper_chol = chol(inv(H))
%
% In other words:
% G ~ IW(m, v, H) where H = inv(H_inv_upper_chol'*H_inv_upper_chol)
% or, equivalently, using the correspondence between Wishart and
% inverse-Wishart:
% inv(G) ~ W(m, v, S) where S = H_inv_upper_chol'*H_inv_upper_chol = inv(H)
% INPUTS:
% m: dimension of G and H_inv_upper_chol.
% v: degrees of freedom, greater or equal than m.
% H_inv_chol: upper cholesky decomposition of the inverse of the
% matrix parameter.
% The upper cholesky of the inverse is requested here
% in order to avoid to recompute it at every random draw.
% H_inv_upper_chol = chol(inv(H))
% OUTPUTS:
% G: G ~ IW(m, v, H) where H = inv(H_inv_upper_chol'*H_inv_upper_chol)
% or, equivalently, using the correspondence between Wishart and
% inverse-Wishart: inv(G) ~ W(m, v, S) where
% S = H_inv_upper_chol'*H_inv_upper_chol = inv(H)
%
% SPECIAL REQUIREMENT
% none
%
% part of DYNARE, copyright Dynare Team (2003-2008)
% Gnu Public License.
X = randn(v, m) * H_inv_upper_chol;