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git-svn-id: https://www.dynare.org/svn/dynare/dynare_v4@1615 ac1d8469-bf42-47a9-8791-bf33cf982152time-shift
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function G = rand_inverse_wishart(m, v, H_inv_upper_chol)
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% function G = rand_inverse_wishart(m, v, H_inv_upper_chol)
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% rand_inverse_wishart Pseudo random matrices drawn from an
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% inverse Wishart distribution
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%
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% inverse Wishart distribution
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% G = rand_inverse_wishart(m, v, H_inv_upper_chol)
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%
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% Returns an m-by-m matrix drawn from an inverse-Wishart distribution.
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%
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% m: dimension of G and H_inv_upper_chol.
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% v: degrees of freedom, greater or equal than m.
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% H_inv_chol: upper cholesky decomposition of the inverse of the
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% matrix parameter.
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% The upper cholesky of the inverse is requested here
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% in order to avoid to recompute it at every random draw.
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% H_inv_upper_chol = chol(inv(H))
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%
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% In other words:
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% G ~ IW(m, v, H) where H = inv(H_inv_upper_chol'*H_inv_upper_chol)
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% or, equivalently, using the correspondence between Wishart and
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% inverse-Wishart:
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% inv(G) ~ W(m, v, S) where S = H_inv_upper_chol'*H_inv_upper_chol = inv(H)
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% INPUTS:
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% m: dimension of G and H_inv_upper_chol.
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% v: degrees of freedom, greater or equal than m.
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% H_inv_chol: upper cholesky decomposition of the inverse of the
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% matrix parameter.
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% The upper cholesky of the inverse is requested here
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% in order to avoid to recompute it at every random draw.
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% H_inv_upper_chol = chol(inv(H))
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% OUTPUTS:
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% G: G ~ IW(m, v, H) where H = inv(H_inv_upper_chol'*H_inv_upper_chol)
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% or, equivalently, using the correspondence between Wishart and
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% inverse-Wishart: inv(G) ~ W(m, v, S) where
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% S = H_inv_upper_chol'*H_inv_upper_chol = inv(H)
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%
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% SPECIAL REQUIREMENT
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% none
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%
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% part of DYNARE, copyright Dynare Team (2003-2008)
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% Gnu Public License.
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X = randn(v, m) * H_inv_upper_chol;
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