97 lines
3.9 KiB
Matlab
97 lines
3.9 KiB
Matlab
function [x,f,abscissa,dens,binf,bsup] = draw_prior_density(indx,bayestopt_)
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% Computes values of prior densities at many points (before plotting)
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%
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% INPUTS
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% indx [integer] Parameter number.
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% bayestopt_ [structure] Describes the prior beliefs.
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%
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% OUTPUTS
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% x [double] Row vector, subset of 'abscissa' such as the density is less than 10
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% f [double] Row vector, subset of 'dens' such as the density is less than 10
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% abscissa [double] Row vector, abscissa
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% dens [double] Row vector, density
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% binf: [double] Scalar, first element of x
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% bsup: [double] Scalar, last element of x
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% Copyright © 2004-2023 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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pshape = bayestopt_.pshape;
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p3 = bayestopt_.p3;
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p4 = bayestopt_.p4;
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p6 = bayestopt_.p6;
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p7 = bayestopt_.p7;
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truncprior = 1e-3;
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steps = 200;
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switch pshape(indx)
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case 1% Beta prior
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density = @(x,a,b,aa,bb) betapdf((x-aa)/(bb-aa), a, b)/(bb-aa);
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infbound = betainv(truncprior,p6(indx),p7(indx))*(p4(indx)-p3(indx))+p3(indx);
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supbound = betainv(1-truncprior,p6(indx),p7(indx))*(p4(indx)-p3(indx))+p3(indx);
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abscissa = linspace(infbound,supbound,steps);
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dens = density(abscissa,p6(indx),p7(indx),p3(indx),p4(indx));
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case 2% Generalized Gamma prior
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density = @(x,a,b,c) gampdf(x-c,a,b);
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infbound = gaminv(truncprior,p6(indx),p7(indx))+p3(indx);
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supbound = gaminv(1-truncprior,p6(indx),p7(indx))+p3(indx);
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abscissa = linspace(infbound,supbound,steps);
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dens = density(abscissa,p6(indx),p7(indx),p3(indx));
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case 3% Gaussian prior
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infbound = max(p3(indx),norminv(truncprior,p6(indx),p7(indx)));
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supbound = min(p4(indx),norminv(1-truncprior,p6(indx),p7(indx)));
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abscissa = linspace(infbound,supbound,steps);
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dens = normpdf(abscissa,p6(indx),p7(indx));
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case 4% Inverse-gamma of type 1 prior
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infbound = 1/sqrt(gaminv(1-10*truncprior, p7(indx)/2, 2/p6(indx)))+p3(indx);
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supbound = 1/sqrt(gaminv(10*truncprior, p7(indx)/2, 2/p6(indx)))+p3(indx);
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abscissa = linspace(infbound,supbound,steps);
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dens = exp(lpdfig1(abscissa-p3(indx),p6(indx),p7(indx)));
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case 5% Uniform prior
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infbound = p6(indx);
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supbound = p7(indx);
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abscissa = linspace(infbound,supbound,steps);
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dens = ones(1, steps) / (supbound-infbound);
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case 6% Inverse-gamma of type 2 prior
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infbound = 1/(gaminv(1-10*truncprior, p7(indx)/2, 2/p6(indx)))+p3(indx);
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supbound = 1/(gaminv(10*truncprior, p7(indx)/2, 2/p6(indx)))+p3(indx);
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abscissa = linspace(infbound,supbound,steps);
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dens = exp(lpdfig2(abscissa-p3(indx),p6(indx),p7(indx)));
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case 8
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density = @(x,a,b,c) exp(lpdfgweibull(x, a, b, c));
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infbound = p3(indx)+wblinv(truncprior,p6(indx),p7(indx));
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supbound = p3(indx)+wblinv(1-truncprior,p6(indx),p7(indx));
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abscissa = linspace(infbound,supbound,steps);
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dens = density(abscissa,p6(indx),p7(indx),p3(indx));
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otherwise
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error('draw_prior_density: unknown distribution shape (index %d, type %d)', indx, pshape(indx));
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end
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if pshape(indx) ~= 5
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[~,k1] = max(dens);
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if k1 == 1 || k1 == length(dens)
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k = find(dens > 10);
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dens(k) = NaN;
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end
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end
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binf = abscissa(1);
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bsup = abscissa(end);
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x = abscissa;
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f = dens;
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