commit
fb77b447bd
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@ -22,7 +22,7 @@ function m = compute_prior_mode(hyperparameters,shape)
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% [3] The uniform distribution has an infinity of modes. In this case the function returns the prior mean.
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% [4] For the beta distribution we can have 1, 2 or an infinity of modes.
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% Copyright (C) 2009 Dynare Team
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% Copyright (C) 2009-2015 Dynare Team
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%
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% This file is part of Dynare.
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%
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@ -74,7 +74,7 @@ switch shape
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m = m + hyperparameters(3);
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end
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case 5
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m = .5*(hyperparameters(2)-hyperparameters(1)) ;
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m = hyperparameters(1);
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case 6
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% s = hyperparameters(1)
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% nu = hyperparameters(2)
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@ -1,4 +1,4 @@
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function pdraw = prior_draw(init,uniform)
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function pdraw = prior_draw(init,uniform) % --*-- Unitary tests --*--
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% This function generate one draw from the joint prior distribution.
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%
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% INPUTS
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@ -15,7 +15,7 @@ function pdraw = prior_draw(init,uniform)
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% NOTE 1. Input arguments 1 an 2 are only needed for initialization.
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% NOTE 2. A given draw from the joint prior distribution does not satisfy BK conditions a priori.
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% Copyright (C) 2006-2010 Dynare Team
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% Copyright (C) 2006-2015 Dynare Team
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%
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% This file is part of Dynare.
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%
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@ -93,6 +93,11 @@ end
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if uniform_draws
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pdraw(uniform_index) = rand(length(uniform_index),1).*(p4(uniform_index)-p3(uniform_index)) + p3(uniform_index);
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out_of_bound = find( (pdraw(uniform_index)'>ub(uniform_index)) | (pdraw(uniform_index)'<lb(uniform_index)));
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while ~isempty(out_of_bound),
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pdraw(uniform_index) = rand(length(uniform_index),1).*(p4(uniform_index)-p3(uniform_index)) + p3(uniform_index);
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out_of_bound = find( (pdraw(uniform_index)'>ub(uniform_index)) | (pdraw(uniform_index)'<lb(uniform_index)));
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end
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end
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if gaussian_draws
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@ -139,7 +144,374 @@ if inverse_gamma_2_draws
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out_of_bound = find( (pdraw(inverse_gamma_2_index)'>ub(inverse_gamma_2_index)) | (pdraw(inverse_gamma_2_index)'<lb(inverse_gamma_2_index)));
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while ~isempty(out_of_bound),
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pdraw(inverse_gamma_2_index(out_of_bound)) = ...
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sqrt(1./gamrnd(p7(inverse_gamma_2_index(out_of_bound))/2,2./p6(inverse_gamma_2_index(out_of_bound))))+p3(inverse_gamma_2_index(out_of_bound));
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1./gamrnd(p7(inverse_gamma_2_index(out_of_bound))/2,2./p6(inverse_gamma_2_index(out_of_bound)))+p3(inverse_gamma_2_index(out_of_bound));
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out_of_bound = find( (pdraw(inverse_gamma_2_index)'>ub(inverse_gamma_2_index)) | (pdraw(inverse_gamma_2_index)'<lb(inverse_gamma_2_index)));
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end
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end
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%@test:1
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%$ %% Initialize required structures
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%$ options_.prior_trunc=0;
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%$ options_.initialize_estimated_parameters_with_the_prior_mode=0;
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%$
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%$ M_.dname='test';
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%$ M_.param_names = 'alp';
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%$ ndraws=100000;
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%$ global estim_params_
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%$ estim_params_.var_exo = [];
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%$ estim_params_.var_endo = [];
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%$ estim_params_.corrx = [];
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%$ estim_params_.corrn = [];
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%$ estim_params_.param_vals = [];
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%$ estim_params_.param_vals = [1, NaN, (-Inf), Inf, 1, 0.356, 0.02, NaN, NaN, NaN ];
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%$
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%$ %% beta
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%$ estim_params_.param_vals(1,3)= -Inf;%LB
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%$ estim_params_.param_vals(1,4)= +Inf;%UB
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%$ estim_params_.param_vals(1,5)= 1;%Shape
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%$ estim_params_.param_vals(1,6)=0.5;
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%$ estim_params_.param_vals(1,7)=0.01;
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%$ estim_params_.param_vals(1,8)=NaN;
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%$ estim_params_.param_vals(1,9)=NaN;
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%$
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%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
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%$
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%$ pdraw = prior_draw(1,0);
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%$ pdraw_vec=NaN(ndraws,1);
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%$ for ii=1:ndraws
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%$ pdraw_vec(ii)=prior_draw(0,0);
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%$ end
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%$
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%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-4 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-4 || any(pdraw_vec<0) || any(pdraw_vec>1)
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%$ error('Beta prior wrong')
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%$ end
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%$
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%$
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%$ %% Gamma
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%$ estim_params_.param_vals(1,3)= -Inf;%LB
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%$ estim_params_.param_vals(1,4)= +Inf;%UB
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%$ estim_params_.param_vals(1,5)= 2;%Shape
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%$ estim_params_.param_vals(1,6)=0.5;
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%$ estim_params_.param_vals(1,7)=0.01;
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%$ estim_params_.param_vals(1,8)=NaN;
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%$ estim_params_.param_vals(1,9)=NaN;
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%$
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%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
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%$
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%$ pdraw = prior_draw(1,0);
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%$ pdraw_vec=NaN(ndraws,1);
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%$ for ii=1:ndraws
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%$ pdraw_vec(ii)=prior_draw(0,0);
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%$ end
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%$
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%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-4 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-4 || any(pdraw_vec<0)
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%$ error('Gamma prior wrong')
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%$ end
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%$
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%$ %% Normal
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%$ estim_params_.param_vals(1,3)= -Inf;%LB
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%$ estim_params_.param_vals(1,4)= +Inf;%UB
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%$ estim_params_.param_vals(1,5)= 3;%Shape
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%$ estim_params_.param_vals(1,6)=0.5;
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%$ estim_params_.param_vals(1,7)=0.01;
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%$ estim_params_.param_vals(1,8)=NaN;
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%$ estim_params_.param_vals(1,9)=NaN;
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%$
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%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
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%$
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%$ pdraw = prior_draw(1,0);
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%$ pdraw_vec=NaN(ndraws,1);
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%$ for ii=1:ndraws
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%$ pdraw_vec(ii)=prior_draw(0,0);
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%$ end
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%$
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%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-4 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-4
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%$ error('Normal prior wrong')
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%$ end
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%$
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%$ %% inverse gamma distribution (type 1)
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%$ estim_params_.param_vals(1,3)= -Inf;%LB
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%$ estim_params_.param_vals(1,4)= +Inf;%UB
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%$ estim_params_.param_vals(1,5)= 4;%Shape
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%$ estim_params_.param_vals(1,6)=0.5;
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%$ estim_params_.param_vals(1,7)=0.01;
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%$ estim_params_.param_vals(1,8)=NaN;
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%$ estim_params_.param_vals(1,9)=NaN;
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%$
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%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
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%$
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%$ pdraw = prior_draw(1,0);
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%$ pdraw_vec=NaN(ndraws,1);
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%$ for ii=1:ndraws
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%$ pdraw_vec(ii)=prior_draw(0,0);
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%$ end
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%$
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%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-4 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-4 || any(pdraw_vec<0)
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%$ error('inverse gamma distribution (type 1) prior wrong')
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%$ end
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%$
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%$ %% Uniform
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%$ estim_params_.param_vals(1,3)= -Inf;%LB
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%$ estim_params_.param_vals(1,4)= +Inf;%UB
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%$ estim_params_.param_vals(1,5)= 5;%Shape
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%$ estim_params_.param_vals(1,6)=0.5;
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%$ estim_params_.param_vals(1,7)=sqrt(12)^(-1)*(1-0);
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%$ estim_params_.param_vals(1,8)=NaN;
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%$ estim_params_.param_vals(1,9)=NaN;
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%$
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%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
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%$
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%$ pdraw = prior_draw(1,0);
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%$ pdraw_vec=NaN(ndraws,1);
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%$ for ii=1:ndraws
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%$ pdraw_vec(ii)=prior_draw(0,0);
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%$ end
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%$
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%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-2 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-3 || any(pdraw_vec<0) || any(pdraw_vec>1)
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%$ error('Uniform prior wrong')
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%$ end
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%$
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%$ %% inverse gamma distribution (type 2)
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%$ estim_params_.param_vals(1,3)= -Inf;%LB
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%$ estim_params_.param_vals(1,4)= +Inf;%UB
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%$ estim_params_.param_vals(1,5)= 6;%Shape
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%$ estim_params_.param_vals(1,6)=0.5;
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%$ estim_params_.param_vals(1,7)=0.01;
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%$ estim_params_.param_vals(1,8)=NaN;
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%$ estim_params_.param_vals(1,9)=NaN;
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%$
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%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
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%$
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%$ pdraw = prior_draw(1,0);
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%$ pdraw_vec=NaN(ndraws,1);
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%$ for ii=1:ndraws
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%$ pdraw_vec(ii)=prior_draw(0,0);
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%$ end
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%$
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%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-4 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-4 || any(pdraw_vec<0)
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%$ error('inverse gamma distribution (type 2) prior wrong')
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%$ end
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%$
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%$
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%$ %%%%%%%%%%%%%%%%%%%%%% Generalized distributions %%%%%%%%%%%%%%%%%%%%%
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%$
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%$ %% beta
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%$ estim_params_.param_vals(1,3)= -Inf;%LB
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%$ estim_params_.param_vals(1,4)= +Inf;%UB
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%$ estim_params_.param_vals(1,5)= 1;%Shape
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%$ estim_params_.param_vals(1,6)=1.5;
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%$ estim_params_.param_vals(1,7)=0.01;
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%$ estim_params_.param_vals(1,8)=1;
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%$ estim_params_.param_vals(1,9)=3;
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%$
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%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
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%$
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%$ pdraw = prior_draw(1,0);
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%$ pdraw_vec=NaN(ndraws,1);
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%$ for ii=1:ndraws
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%$ pdraw_vec(ii)=prior_draw(0,0);
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%$ end
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%$
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%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-4 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-4 || any(pdraw_vec<estim_params_.param_vals(1,3)) || any(pdraw_vec>estim_params_.param_vals(1,4))
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%$ error('Beta prior wrong')
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%$ end
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%$
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%$ %% Gamma
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%$ estim_params_.param_vals(1,3)= -Inf;%LB
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%$ estim_params_.param_vals(1,4)= +Inf;%UB
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%$ estim_params_.param_vals(1,5)= 2;%Shape
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%$ estim_params_.param_vals(1,6)=1.5;
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%$ estim_params_.param_vals(1,7)=0.01;
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%$ estim_params_.param_vals(1,8)=1;
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%$ estim_params_.param_vals(1,9)=NaN;
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%$
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%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
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%$
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%$ pdraw = prior_draw(1,0);
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%$ pdraw_vec=NaN(ndraws,1);
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%$ for ii=1:ndraws
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%$ pdraw_vec(ii)=prior_draw(0,0);
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%$ end
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%$
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%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-4 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-4 || any(pdraw_vec<estim_params_.param_vals(1,8))
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%$ error('Gamma prior wrong')
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%$ end
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%$
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%$ %% inverse gamma distribution (type 1)
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%$ estim_params_.param_vals(1,3)= -Inf;%LB
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%$ estim_params_.param_vals(1,4)= +Inf;%UB
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%$ estim_params_.param_vals(1,5)= 4;%Shape
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%$ estim_params_.param_vals(1,6)=1.5;
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%$ estim_params_.param_vals(1,7)=0.01;
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%$ estim_params_.param_vals(1,8)=1;
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%$ estim_params_.param_vals(1,9)=NaN;
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%$
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%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
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%$
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%$ pdraw = prior_draw(1,0);
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%$ pdraw_vec=NaN(ndraws,1);
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%$ for ii=1:ndraws
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%$ pdraw_vec(ii)=prior_draw(0,0);
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%$ end
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%$
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%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-4 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-4 || any(pdraw_vec<estim_params_.param_vals(1,8))
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%$ error('inverse gamma distribution (type 1) prior wrong')
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%$ end
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%$
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%$ %% Uniform
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%$ estim_params_.param_vals(1,3)= -Inf;%LB
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%$ estim_params_.param_vals(1,4)= +Inf;%UB
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%$ estim_params_.param_vals(1,5)= 5;%Shape
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%$ estim_params_.param_vals(1,6)=1.5;
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%$ estim_params_.param_vals(1,7)=0.01;
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%$ estim_params_.param_vals(1,8)=NaN;
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%$ estim_params_.param_vals(1,9)=NaN;
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%$
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%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
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%$
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%$ pdraw = prior_draw(1,0);
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%$ pdraw_vec=NaN(ndraws,1);
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%$ for ii=1:ndraws
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%$ pdraw_vec(ii)=prior_draw(0,0);
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%$ end
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%$
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%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-4 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-4 || any(pdraw_vec<estim_params_.param_vals(1,3)) || any(pdraw_vec>estim_params_.param_vals(1,4))
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%$ error('Uniform prior wrong')
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%$ end
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%$
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%$ %% inverse gamma distribution (type 2)
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%$ estim_params_.param_vals(1,3)= -Inf;%LB
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%$ estim_params_.param_vals(1,4)= +Inf;%UB
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%$ estim_params_.param_vals(1,5)= 6;%Shape
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%$ estim_params_.param_vals(1,6)=1.5;
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%$ estim_params_.param_vals(1,7)=0.01;
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%$ estim_params_.param_vals(1,8)=1;
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%$ estim_params_.param_vals(1,9)=NaN;
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%$
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%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
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%$
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%$ pdraw = prior_draw(1,0);
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%$ pdraw_vec=NaN(ndraws,1);
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%$ for ii=1:ndraws
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%$ pdraw_vec(ii)=prior_draw(0,0);
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%$ end
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%$
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%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>1e-4 || abs(std(pdraw_vec)-estim_params_.param_vals(1,7))>1e-4 || any(pdraw_vec<estim_params_.param_vals(1,8))
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%$ error('inverse gamma distribution (type 2) prior wrong')
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%$ end
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%$
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%$ %%%%%%%%%%%% With prior truncation
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%$ options_.prior_trunc=.4;
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%$
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%$ %% beta
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%$ estim_params_.param_vals(1,3)= -Inf;%LB
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%$ estim_params_.param_vals(1,4)= +Inf;%UB
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%$ estim_params_.param_vals(1,5)= 1;%Shape
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%$ estim_params_.param_vals(1,6)=1.5;
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%$ estim_params_.param_vals(1,7)=0.01;
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%$ estim_params_.param_vals(1,8)=1;
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%$ estim_params_.param_vals(1,9)=3;
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%$
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%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
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%$ bounds = prior_bounds(bayestopt_,options_)';
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%$
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%$ pdraw = prior_draw(1,0);
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%$ pdraw_vec=NaN(ndraws,1);
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%$ for ii=1:ndraws
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%$ pdraw_vec(ii)=prior_draw(0,0);
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%$ end
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%$
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%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>5e-3 || any(pdraw_vec<bounds.lb) || any(pdraw_vec>bounds.ub)
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%$ error('Beta prior wrong')
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%$ end
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%$
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%$ %% Gamma
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%$ estim_params_.param_vals(1,3)= -Inf;%LB
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%$ estim_params_.param_vals(1,4)= +Inf;%UB
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%$ estim_params_.param_vals(1,5)= 2;%Shape
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%$ estim_params_.param_vals(1,6)=1.5;
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%$ estim_params_.param_vals(1,7)=0.01;
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%$ estim_params_.param_vals(1,8)=1;
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%$ estim_params_.param_vals(1,9)=NaN;
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%$
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%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
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%$ bounds = prior_bounds(bayestopt_,options_)';
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%$
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%$ pdraw = prior_draw(1,0);
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%$ pdraw_vec=NaN(ndraws,1);
|
||||
%$ for ii=1:ndraws
|
||||
%$ pdraw_vec(ii)=prior_draw(0,0);
|
||||
%$ end
|
||||
%$
|
||||
%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>5e-3 || any(pdraw_vec<bounds.lb) || any(pdraw_vec>bounds.ub)
|
||||
%$ error('Gamma prior wrong')
|
||||
%$ end
|
||||
%$
|
||||
%$ %% inverse gamma distribution (type 1)
|
||||
%$ estim_params_.param_vals(1,3)= -Inf;%LB
|
||||
%$ estim_params_.param_vals(1,4)= +Inf;%UB
|
||||
%$ estim_params_.param_vals(1,5)= 4;%Shape
|
||||
%$ estim_params_.param_vals(1,6)=1.5;
|
||||
%$ estim_params_.param_vals(1,7)=0.01;
|
||||
%$ estim_params_.param_vals(1,8)=1;
|
||||
%$ estim_params_.param_vals(1,9)=NaN;
|
||||
%$
|
||||
%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
|
||||
%$ bounds = prior_bounds(bayestopt_,options_)';
|
||||
%$
|
||||
%$ pdraw = prior_draw(1,0);
|
||||
%$ pdraw_vec=NaN(ndraws,1);
|
||||
%$ for ii=1:ndraws
|
||||
%$ pdraw_vec(ii)=prior_draw(0,0);
|
||||
%$ end
|
||||
%$
|
||||
%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>5e-3 || any(pdraw_vec<bounds.lb) || any(pdraw_vec>bounds.ub)
|
||||
%$ error('inverse gamma distribution (type 1) prior wrong')
|
||||
%$ end
|
||||
%$
|
||||
%$ %% Uniform
|
||||
%$ estim_params_.param_vals(1,3)= -Inf;%LB
|
||||
%$ estim_params_.param_vals(1,4)= +Inf;%UB
|
||||
%$ estim_params_.param_vals(1,5)= 5;%Shape
|
||||
%$ estim_params_.param_vals(1,6)=1.5;
|
||||
%$ estim_params_.param_vals(1,7)=0.01;
|
||||
%$ estim_params_.param_vals(1,8)=NaN;
|
||||
%$ estim_params_.param_vals(1,9)=NaN;
|
||||
%$
|
||||
%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
|
||||
%$ bounds = prior_bounds(bayestopt_,options_)';
|
||||
%$
|
||||
%$ pdraw = prior_draw(1,0);
|
||||
%$ pdraw_vec=NaN(ndraws,1);
|
||||
%$ for ii=1:ndraws
|
||||
%$ pdraw_vec(ii)=prior_draw(0,0);
|
||||
%$ end
|
||||
%$
|
||||
%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>5e-3 || any(pdraw_vec<bounds.lb) || any(pdraw_vec>bounds.ub)
|
||||
%$ error('Uniform prior wrong')
|
||||
%$ end
|
||||
%$
|
||||
%$
|
||||
%$ %% inverse gamma distribution (type 2)
|
||||
%$ estim_params_.param_vals(1,3)= -Inf;%LB
|
||||
%$ estim_params_.param_vals(1,4)= +Inf;%UB
|
||||
%$ estim_params_.param_vals(1,5)= 6;%Shape
|
||||
%$ estim_params_.param_vals(1,6)=1.5;
|
||||
%$ estim_params_.param_vals(1,7)=0.01;
|
||||
%$ estim_params_.param_vals(1,8)=1;
|
||||
%$ estim_params_.param_vals(1,9)=NaN;
|
||||
%$
|
||||
%$ [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
|
||||
%$ bounds = prior_bounds(bayestopt_,options_)';
|
||||
%$
|
||||
%$ pdraw = prior_draw(1,0);
|
||||
%$ pdraw_vec=NaN(ndraws,1);
|
||||
%$ for ii=1:ndraws
|
||||
%$ pdraw_vec(ii)=prior_draw(0,0);
|
||||
%$ end
|
||||
%$
|
||||
%$ if abs(mean(pdraw_vec)-estim_params_.param_vals(1,6))>5e-3 || any(pdraw_vec<bounds.lb) || any(pdraw_vec>bounds.ub)
|
||||
%$ error('inverse gamma distribution (type 2) prior wrong')
|
||||
%$ end
|
||||
%$
|
||||
%@eof:1
|
||||
|
|
|
@ -196,6 +196,9 @@ k2 = find(isnan(bayestopt_.p4(k)));
|
|||
bayestopt_.p3(k(k1)) = zeros(length(k1),1);
|
||||
bayestopt_.p4(k(k2)) = Inf(length(k2),1);
|
||||
for i=1:length(k)
|
||||
if (bayestopt_.p1(k(i))<bayestopt_.p3(k(i))) || (bayestopt_.p1(k(i))>bayestopt_.p4(k(i)))
|
||||
error(['The prior mean of ' bayestopt_.name{k(i)} ' has to be above the lower (' num2str(bayestopt_.p3(k(i))) ') bound of the Gamma prior density!']);
|
||||
end
|
||||
if isinf(bayestopt_.p2(k(i)))
|
||||
error(['Infinite prior standard deviation for parameter ' bayestopt_.name{k(i)} ' is not allowed (Gamma prior)!'])
|
||||
end
|
||||
|
@ -224,6 +227,9 @@ k2 = find(isnan(bayestopt_.p4(k)));
|
|||
bayestopt_.p3(k(k1)) = zeros(length(k1),1);
|
||||
bayestopt_.p4(k(k2)) = Inf(length(k2),1);
|
||||
for i=1:length(k)
|
||||
if (bayestopt_.p1(k(i))<bayestopt_.p3(k(i))) || (bayestopt_.p1(k(i))>bayestopt_.p4(k(i)))
|
||||
error(['The prior mean of ' bayestopt_.name{k(i)} ' has to be above the lower (' num2str(bayestopt_.p3(k(i))) ') bound of the Inverse Gamma prior density!']);
|
||||
end
|
||||
[bayestopt_.p6(k(i)),bayestopt_.p7(k(i))] = ...
|
||||
inverse_gamma_specification(bayestopt_.p1(k(i))-bayestopt_.p3(k(i)),bayestopt_.p2(k(i)),1,0) ;
|
||||
bayestopt_.p5(k(i)) = compute_prior_mode([ bayestopt_.p6(k(i)) , bayestopt_.p7(k(i)) , bayestopt_.p3(k(i)) ], 4) ;
|
||||
|
@ -246,6 +252,9 @@ k2 = find(isnan(bayestopt_.p4(k)));
|
|||
bayestopt_.p3(k(k1)) = zeros(length(k1),1);
|
||||
bayestopt_.p4(k(k2)) = Inf(length(k2),1);
|
||||
for i=1:length(k)
|
||||
if (bayestopt_.p1(k(i))<bayestopt_.p3(k(i))) || (bayestopt_.p1(k(i))>bayestopt_.p4(k(i)))
|
||||
error(['The prior mean of ' bayestopt_.name{k(i)} ' has to be above the lower (' num2str(bayestopt_.p3(k(i))) ') bound of the Inverse Gamma II prior density!']);
|
||||
end
|
||||
[bayestopt_.p6(k(i)),bayestopt_.p7(k(i))] = ...
|
||||
inverse_gamma_specification(bayestopt_.p1(k(i))-bayestopt_.p3(k(i)),bayestopt_.p2(k(i)),2,0);
|
||||
bayestopt_.p5(k(i)) = compute_prior_mode([ bayestopt_.p6(k(i)) , bayestopt_.p7(k(i)) , bayestopt_.p3(k(i)) ], 6) ;
|
||||
|
|
Loading…
Reference in New Issue