198 lines
6.8 KiB
Matlab
198 lines
6.8 KiB
Matlab
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function p = draw(o)
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% Return a random draw from the prior distribution.
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%
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% INPUTS
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% - o [dprior]
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%
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% OUTPUTS
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% - p [double] m×1 vector, random draw from the prior distribution (m is the number of estimated parameters).
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%
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% REMARKS
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% None.
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%
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% EXAMPLE
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%
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% >> Prior = dprior(bayestopt_, options_.prior_trunc);
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% >> d = Prior.draw()
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% Copyright © 2023 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 <https://www.gnu.org/licenses/>.
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p = NaN(rows(o.lb), 1);
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if o.isuniform
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p(o.iduniform) = rand(length(o.iduniform),1).*(o.p4(o.iduniform)-o.p3(o.iduniform)) + o.p3(o.iduniform);
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oob = find( (p(o.iduniform)>o.ub(o.iduniform)) | (p(o.iduniform)<o.lb(o.iduniform)));
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while ~isempty(oob)
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p(o.iduniform) = rand(length(o.iduniform), 1).*(o.p4(o.iduniform)-o.p3(o.iduniform)) + o.p3(o.iduniform);
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oob = find( (p(o.iduniform)>o.ub(o.iduniform)) | (p(o.iduniform)<o.lb(o.iduniform)));
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end
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end
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if o.isgaussian
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p(o.idgaussian) = randn(length(o.idgaussian), 1).*o.p7(o.idgaussian) + o.p6(o.idgaussian);
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oob = find( (p(o.idgaussian)>o.ub(o.idgaussian)) | (p(o.idgaussian)<o.lb(o.idgaussian)));
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while ~isempty(oob)
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p(o.idgaussian(oob)) = randn(length(o.idgaussian(oob)), 1).*o.p7(o.idgaussian(oob)) + o.p6(o.idgaussian(oob));
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oob = find( (p(o.idgaussian)>o.ub(o.idgaussian)) | (p(o.idgaussian)<o.lb(o.idgaussian)));
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end
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end
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if o.isgamma
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p(o.idgamma) = gamrnd(o.p6(o.idgamma), o.p7(o.idgamma))+o.p3(o.idgamma);
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oob = find( (p(o.idgamma)>o.ub(o.idgamma)) | (p(o.idgamma)<o.lb(o.idgamma)));
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while ~isempty(oob)
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p(o.idgamma(oob)) = gamrnd(o.p6(o.idgamma(oob)), o.p7(o.idgamma(oob)))+o.p3(o.idgamma(oob));
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oob = find( (p(o.idgamma)>o.ub(o.idgamma)) | (p(o.idgamma)<o.lb(o.idgamma)));
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end
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end
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if o.isbeta
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p(o.idbeta) = (o.p4(o.idbeta)-o.p3(o.idbeta)).*betarnd(o.p6(o.idbeta), o.p7(o.idbeta))+o.p3(o.idbeta);
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oob = find( (p(o.idbeta)>o.ub(o.idbeta)) | (p(o.idbeta)<o.lb(o.idbeta)));
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while ~isempty(oob)
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p(o.idbeta(oob)) = (o.p4(o.idbeta(oob))-o.p3(o.idbeta(oob))).*betarnd(o.p6(o.idbeta(oob)), o.p7(o.idbeta(oob)))+o.p3(o.idbeta(oob));
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oob = find( (p(o.idbeta)>o.ub(o.idbeta)) | (p(o.idbeta)<o.lb(o.idbeta)));
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end
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end
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if o.isinvgamma1
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p(o.idinvgamma1) = ...
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sqrt(1./gamrnd(o.p7(o.idinvgamma1)/2, 2./o.p6(o.idinvgamma1)))+o.p3(o.idinvgamma1);
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oob = find( (p(o.idinvgamma1)>o.ub(o.idinvgamma1)) | (p(o.idinvgamma1)<o.lb(o.idinvgamma1)));
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while ~isempty(oob)
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p(o.idinvgamma1(oob)) = ...
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sqrt(1./gamrnd(o.p7(o.idinvgamma1(oob))/2, 2./o.p6(o.idinvgamma1(oob))))+o.p3(o.idinvgamma1(oob));
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oob = find( (p(o.idinvgamma1)>o.ub(o.idinvgamma1)) | (p(o.idinvgamma1)<o.lb(o.idinvgamma1)));
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end
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end
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if o.isinvgamma2
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p(o.idinvgamma2) = ...
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1./gamrnd(o.p7(o.idinvgamma2)/2, 2./o.p6(o.idinvgamma2))+o.p3(o.idinvgamma2);
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oob = find( (p(o.idinvgamma2)>o.ub(o.idinvgamma2)) | (p(o.idinvgamma2)<o.lb(o.idinvgamma2)));
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while ~isempty(oob)
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p(o.idinvgamma2(oob)) = ...
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1./gamrnd(o.p7(o.idinvgamma2(oob))/2, 2./o.p6(o.idinvgamma2(oob)))+o.p3(o.idinvgamma2(oob));
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oob = find( (p(o.idinvgamma2)>o.ub(o.idinvgamma2)) | (p(o.idinvgamma2)<o.lb(o.idinvgamma2)));
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end
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end
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if o.isweibull
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p(o.idweibull) = wblrnd(o.p7(o.idweibull), o.p6(o.idweibull)) + o.p3(o.idweibull);
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oob = find( (p(o.idweibull)>o.ub(o.idweibull)) | (p(o.idweibull)<o.lb(o.idweibull)));
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while ~isempty(oob)
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p(o.idweibull(oob)) = wblrnd(o.p7(o.idweibull(oob)), o.p6(o.idweibull(oob)))+o.p3(o.idweibull(oob));
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oob = find( (p(o.idweibull)>o.ub(o.idweibull)) | (p(o.idweibull)<o.lb(o.idweibull)));
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end
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end
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return % --*-- Unit tests --*--
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%@test:1
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% Fill global structures with required fields...
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prior_trunc = 1e-10;
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p0 = repmat([1; 2; 3; 4; 5; 6; 8], 2, 1); % Prior shape
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p1 = .4*ones(14,1); % Prior mean
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p2 = .2*ones(14,1); % Prior std.
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p3 = NaN(14,1);
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p4 = NaN(14,1);
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p5 = NaN(14,1);
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p6 = NaN(14,1);
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p7 = NaN(14,1);
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for i=1:14
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switch p0(i)
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case 1
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% Beta distribution
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p3(i) = 0;
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p4(i) = 1;
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[p6(i), p7(i)] = beta_specification(p1(i), p2(i)^2, p3(i), p4(i));
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p5(i) = compute_prior_mode([p6(i) p7(i)], 1);
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case 2
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% Gamma distribution
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p3(i) = 0;
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p4(i) = Inf;
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[p6(i), p7(i)] = gamma_specification(p1(i), p2(i)^2, p3(i), p4(i));
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p5(i) = compute_prior_mode([p6(i) p7(i)], 2);
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case 3
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% Normal distribution
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p3(i) = -Inf;
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p4(i) = Inf;
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p6(i) = p1(i);
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p7(i) = p2(i);
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p5(i) = p1(i);
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case 4
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% Inverse Gamma (type I) distribution
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p3(i) = 0;
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p4(i) = Inf;
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[p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 1, false);
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p5(i) = compute_prior_mode([p6(i) p7(i)], 4);
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case 5
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% Uniform distribution
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[p1(i), p2(i), p6(i), p7(i)] = uniform_specification(p1(i), p2(i), p3(i), p4(i));
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p3(i) = p6(i);
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p4(i) = p7(i);
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p5(i) = compute_prior_mode([p6(i) p7(i)], 5);
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case 6
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% Inverse Gamma (type II) distribution
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p3(i) = 0;
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p4(i) = Inf;
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[p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 2, false);
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p5(i) = compute_prior_mode([p6(i) p7(i)], 6);
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case 8
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% Weibull distribution
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p3(i) = 0;
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p4(i) = Inf;
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[p6(i), p7(i)] = weibull_specification(p1(i), p2(i)^2, p3(i));
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p5(i) = compute_prior_mode([p6(i) p7(i)], 8);
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otherwise
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error('This density is not implemented!')
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end
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end
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BayesInfo.pshape = p0;
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BayesInfo.p1 = p1;
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BayesInfo.p2 = p2;
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BayesInfo.p3 = p3;
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BayesInfo.p4 = p4;
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BayesInfo.p5 = p5;
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BayesInfo.p6 = p6;
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BayesInfo.p7 = p7;
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ndraws = 1e5;
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m0 = BayesInfo.p1; %zeros(14,1);
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v0 = diag(BayesInfo.p2.^2); %zeros(14);
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% Call the tested routine
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try
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% Instantiate dprior object
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o = dprior(BayesInfo, prior_trunc, false);
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% Do simulations in a loop and estimate recursively the mean and the variance.
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for i = 1:ndraws
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draw = o.draw();
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m1 = m0 + (draw-m0)/i;
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m2 = m1*m1';
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v0 = v0 + ((draw*draw'-m2-v0) + (i-1)*(m0*m0'-m2'))/i;
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m0 = m1;
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end
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t(1) = true;
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catch
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t(1) = false;
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end
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if t(1)
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t(2) = all(abs(m0-BayesInfo.p1)<3e-3);
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t(3) = all(all(abs(v0-diag(BayesInfo.p2.^2))<5e-3));
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end
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T = all(t);
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%@eof:1
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