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function b = admissible(o, d)
% Return true iff d is an admissible draw in a distribution characterized by o.
%
% INPUTS
% - o [dprior] Distribution specification for a n×1 vector of independent continuous random variables
% - d [double] n×1 vector.
%
% OUTPUTS
% - b [logical] scalar.
%
% REMARKS
% None.
%
% EXAMPLE
%
% >> Prior = dprior(bayestopt_, options_.prior_trunc);
% >> d = Prior.draw()
% >> Prior.admissible(d)
% ans =
%
% logical
%
% 1
% Copyright © 2023 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 <https://www.gnu.org/licenses/>.
b = false;
if ~isequal(length(d), length(o.lb))
return
end
if all(d>=o.lb & d<=o.ub)
b = true;
end
return % --*-- Unit tests --*--
%@test:1
% Fill global structures with required fields...
prior_trunc = 1e-10;
p0 = repmat([1; 2; 3; 4; 5; 6; 8], 2, 1); % Prior shape
p1 = .4*ones(14,1); % Prior mean
p2 = .2*ones(14,1); % Prior std.
p3 = NaN(14,1);
p4 = NaN(14,1);
p5 = NaN(14,1);
p6 = NaN(14,1);
p7 = NaN(14,1);
for i=1:14
switch p0(i)
case 1
% Beta distribution
p3(i) = 0;
p4(i) = 1;
[p6(i), p7(i)] = beta_specification(p1(i), p2(i)^2, p3(i), p4(i));
p5(i) = compute_prior_mode([p6(i) p7(i)], 1);
case 2
% Gamma distribution
p3(i) = 0;
p4(i) = Inf;
[p6(i), p7(i)] = gamma_specification(p1(i), p2(i)^2, p3(i), p4(i));
p5(i) = compute_prior_mode([p6(i) p7(i)], 2);
case 3
% Normal distribution
p3(i) = -Inf;
p4(i) = Inf;
p6(i) = p1(i);
p7(i) = p2(i);
p5(i) = p1(i);
case 4
% Inverse Gamma (type I) distribution
p3(i) = 0;
p4(i) = Inf;
[p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 1, false);
p5(i) = compute_prior_mode([p6(i) p7(i)], 4);
case 5
% Uniform distribution
[p1(i), p2(i), p6(i), p7(i)] = uniform_specification(p1(i), p2(i), p3(i), p4(i));
p3(i) = p6(i);
p4(i) = p7(i);
p5(i) = compute_prior_mode([p6(i) p7(i)], 5);
case 6
% Inverse Gamma (type II) distribution
p3(i) = 0;
p4(i) = Inf;
[p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 2, false);
p5(i) = compute_prior_mode([p6(i) p7(i)], 6);
case 8
% Weibull distribution
p3(i) = 0;
p4(i) = Inf;
[p6(i), p7(i)] = weibull_specification(p1(i), p2(i)^2, p3(i));
p5(i) = compute_prior_mode([p6(i) p7(i)], 8);
otherwise
error('This density is not implemented!')
end
end
BayesInfo.pshape = p0;
BayesInfo.p1 = p1;
BayesInfo.p2 = p2;
BayesInfo.p3 = p3;
BayesInfo.p4 = p4;
BayesInfo.p5 = p5;
BayesInfo.p6 = p6;
BayesInfo.p7 = p7;
ndraws = 10;
% Call the tested routine
try
% Instantiate dprior object
o = dprior(BayesInfo, prior_trunc, false);
% Do simulations in a loop and estimate recursively the mean and the variance.
for i = 1:ndraws
draw = o.draw();
if ~o.admissible(draw)
error('problem in admissible')
end
end
t(1) = true;
catch
t(1) = false;
end
T = all(t);
%@eof:1
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