81 lines
2.0 KiB
Matlab
81 lines
2.0 KiB
Matlab
function y = rndprior(bayestopt_)
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% function y = rndprior(bayestopt_)
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% Draws random number from the prior density
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%
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% INPUTS
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% bayestopt_: structure characterizing priors
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%
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% OUTPUTS
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% y: drawn numbers vector
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%
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% SPECIAL REQUIREMENTS
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% none
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% Copyright (C) 2003-2008 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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pshape=bayestopt_.pshape;
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pmean=bayestopt_.pmean;
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p1=bayestopt_.p1;
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p2=bayestopt_.p2;
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p3=bayestopt_.p3;
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p4=bayestopt_.p4;
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for i=1:length(pmean),
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switch pshape(i)
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case 1 % Beta
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mu = (pmean(i)-p3(i))/(p4(i)-p3(i));
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stdd = p2(i)/(p4(i)-p3(i));
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A = (1-mu)*mu^2/stdd^2 - mu;
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B = A*(1/mu - 1);
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y(1,i) = betarnd(A, B);
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y(1,i) = y(1,i) * (p4(i)-p3(i)) + p3(i);
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case 2 % Generalized gamma
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mu = pmean(i)-p3(i);
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B = p2(i)^2/mu;
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A = mu/B;
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y(1,i) = gamrnd(A, B) + p3(i);
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case 3 % Gaussian
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MU = pmean(i);
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SIGMA = p2(i);
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y(1,i) = randn*SIGMA+ MU;
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case 4 % Inverse-gamma type 1
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nu = p2(i);
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s = p1(i);
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y(1,i) = 1/sqrt(gamrnd(nu/2, 2/s));
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case 5 % Uniform
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y(1,i) = rand*(p2(i)-p1(i)) + p1(i);
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case 6 % Inverse-gamma type 2
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nu = p2(i);
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s = p1(i);
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y(1,i) = 1/gamrnd(nu/2, 2/s);
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otherwise
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error(sprintf('rndprior: unknown distribution shape (index %d, type %d)', i, pshape(i)));
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end
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end
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% initial version by Marco Ratto
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