function pdraw = prior_draw(init,uniform) % This function generate one draw from the joint prior distribution. % % INPUTS % o init [integer] scalar equal to 1 (first call) or 0. % o uniform [integer] scalar equal to 1 (first call) or 0. % % OUTPUTS % o pdraw [double] 1*npar vector, draws from the joint prior density. % % % SPECIAL REQUIREMENTS % none % % NOTE 1. Input arguments 1 an 2 are only needed for initialization. % NOTE 2. A given draw from the joint prior distribution does not satisfy BK conditions a priori. % Copyright (C) 2006-2010 Dynare Team % % This file is part of Dynare. % % Dynare is free software: you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by % the Free Software Foundation, either version 3 of the License, or % (at your option) any later version. % % Dynare is distributed in the hope that it will be useful, % but WITHOUT ANY WARRANTY; without even the implied warranty of % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the % GNU General Public License for more details. % % You should have received a copy of the GNU General Public License % along with Dynare. If not, see . persistent p6 p7 p3 p4 lb ub persistent uniform_index gaussian_index gamma_index beta_index inverse_gamma_1_index inverse_gamma_2_index persistent uniform_draws gaussian_draws gamma_draws beta_draws inverse_gamma_1_draws inverse_gamma_2_draws if nargin>0 && init p6 = evalin('base', 'bayestopt_.p6'); p7 = evalin('base', 'bayestopt_.p7'); p3 = evalin('base', 'bayestopt_.p3'); p4 = evalin('base', 'bayestopt_.p4'); lb = evalin('base', 'bayestopt_.lb'); ub = evalin('base', 'bayestopt_.ub'); number_of_estimated_parameters = length(p6); if nargin>1 && uniform prior_shape = repmat(5,number_of_estimated_parameters,1); else prior_shape = evalin('base', 'bayestopt_.pshape'); end beta_index = find(prior_shape==1); if isempty(beta_index) beta_draws = 0; else beta_draws = 1; end gamma_index = find(prior_shape==2); if isempty(gamma_index) gamma_draws = 0; else gamma_draws = 1; end gaussian_index = find(prior_shape==3); if isempty(gaussian_index) gaussian_draws = 0; else gaussian_draws = 1; end inverse_gamma_1_index = find(prior_shape==4); if isempty(inverse_gamma_1_index) inverse_gamma_1_draws = 0; else inverse_gamma_1_draws = 1; end uniform_index = find(prior_shape==5); if isempty(uniform_index) uniform_draws = 0; else uniform_draws = 1; end inverse_gamma_2_index = find(prior_shape==6); if isempty(inverse_gamma_2_index) inverse_gamma_2_draws = 0; else inverse_gamma_2_draws = 1; end pdraw = zeros(number_of_estimated_parameters,1); return end if uniform_draws pdraw(uniform_index) = rand(length(uniform_index),1).*(p4(uniform_index)-p3(uniform_index)) + p3(uniform_index); end if gaussian_draws pdraw(gaussian_index) = randn(length(gaussian_index),1).*p7(gaussian_index) + p6(gaussian_index); out_of_bound = find( (pdraw(gaussian_index)'>ub(gaussian_index)) | (pdraw(gaussian_index)'ub(gaussian_index)) | (pdraw(gaussian_index)'ub(gamma_index)) | (pdraw(gamma_index)'ub(gamma_index)) | (pdraw(gamma_index)'ub(beta_index)) | (pdraw(beta_index)'ub(beta_index)) | (pdraw(beta_index)'ub(inverse_gamma_1_index)) | (pdraw(inverse_gamma_1_index)'ub(inverse_gamma_1_index)) | (pdraw(inverse_gamma_1_index)'ub(inverse_gamma_2_index)) | (pdraw(inverse_gamma_2_index)'ub(inverse_gamma_2_index)) | (pdraw(inverse_gamma_2_index)'