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Draws a vector of candidate deep parameters in the joint prior density. B&K conditions have to be tested on each draw... 

git-svn-id: https://www.dynare.org/svn/dynare/dynare_v4@999 ac1d8469-bf42-47a9-8791-bf33cf982152
time-shift
adjemian 2006-10-28 13:25:20 +00:00
parent e66f3884db
commit 22e0f21220
1 changed files with 97 additions and 68 deletions
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165
matlab/prior_draw.m
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@ -2,13 +2,16 @@ function pdraw = prior_draw(init,cc)
% Build one draw from the prior distribution. % Build one draw from the prior distribution.
% %
% INPUTS % INPUTS
% o SampleSize [integer] Size of the sample to build % o init [integer] scalar equal to 1 (first call) or 0.
% % o cc [double] two columns matrix (same as in
% metropolis.m), constraints over the
% parameter space (upper and lower bounds).
%
% OUTPUTS % OUTPUTS
% None. % o pdraw [double] draw from the joint prior density.
% %
% ALGORITHM % ALGORITHM
% None. % ...
% %
% SPECIAL REQUIREMENTS % SPECIAL REQUIREMENTS
% None. % None.
@ -16,10 +19,10 @@ function pdraw = prior_draw(init,cc)
% %
% part of DYNARE, copyright S. Adjemian, M. Juillard (2006) % part of DYNARE, copyright S. Adjemian, M. Juillard (2006)
% Gnu Public License. % Gnu Public License.
global M_ options_ estim_params_ global M_ options_ estim_params_
persistent fname npar bounds pshape pmean pstd a b p3 p4 persistent fname npar bounds pshape pmean pstd a b p3 p4 condition
if init if init
nvx = estim_params_.nvx; nvx = estim_params_.nvx;
nvn = estim_params_.nvn; nvn = estim_params_.nvn;
ncx = estim_params_.ncx; ncx = estim_params_.ncx;
@ -38,95 +41,121 @@ function pdraw = prior_draw(init,cc)
a = zeros(npar,1); a = zeros(npar,1);
b = zeros(npar,1); b = zeros(npar,1);
if nargin == 2 if nargin == 2
bounds = cc; bounds = cc;
else else
bounds = [-Inf Inf]; bounds = [-Inf Inf];
end end
for i = 1:npar for i = 1:npar
if pshape(i) == 3 switch pshape(i)
b(i) = pstd(i)^2/(pmean(i)-p3(i)); case 3% Gaussian prior
a(i) = (pmean(i)-p3(i))/b(i); b(i) = pstd(i)^2/(pmean(i)-p3(i));
elseif pshape(i) == 1 a(i) = (pmean(i)-p3(i))/b(i);
mu = (p1(i)-p3(i))/(p4(i)-p3(i)); case 1% Beta prior
stdd = p2(i)/(p4(i)-p3(i)); mu = (p1(i)-p3(i))/(p4(i)-p3(i));
a(i) = (1-mu)*mu^2/stdd^2 - mu; stdd = p2(i)/(p4(i)-p3(i));
b(i) = a*(1/mu - 1); a(i) = (1-mu)*mu^2/stdd^2 - mu;
elseif pshape(i) == b(i) = a*(1/mu - 1);
end case 2;%Gamma prior
pdraw = zeros(npar,1); mu = p1(i)-p3(i);
b(i) = p2(i)^2/mu;
a(i) = mu/b;
case {5,4,6}
% Nothing to do here
%
% 4: Inverse gamma, type 1, prior
% p2(i) = nu
% p1(i) = s
% 6: Inverse gamma, type 2, prior
% p2(i) = nu
% p1(i) = s
% 5: Uniform prior
% p3(i) and p4(i) are used.
otherwise
disp('prior_draw :: Error!')
disp('Unknown prior shape.')
return
end
pdraw = zeros(npar,1);
end end
condition = 1; condition = 1;
pdraw = zeros(npar,1); pdraw = zeros(npar,1);
return return
end end
for i = 1:npar for i = 1:npar
switch pshape(i) switch pshape(i)
case 5% Uniform prior. case 5% Uniform prior.
pdraw(i) = rand*(p4(i)-p3(i)) + p3(i); pdraw(i) = rand*(p4(i)-p3(i)) + p3(i);
case 3% Gaussian prior. case 3% Gaussian prior.
while condition while condition
tmp = randn*pstd(i) + pmean(i); tmp = randn*pstd(i) + pmean(i);
if tmp >= bounds(i,1) && tmp <= bounds(i,2) if tmp >= bounds(i,1) && tmp <= bounds(i,2)
pdraw(i) = tmp; pdraw(i) = tmp;
break break
end end
end end
case 2% Gamma prior. case 2% Gamma prior.
while condition while condition
g = gamma_draw(a(i),b(i),p3(i)); g = gamma_draw(a(i),b(i),p3(i));
if g >= bounds(i,1) && g <= bounds(i,2) if g >= bounds(i,1) && g <= bounds(i,2)
pdraw(i) = g; pdraw(i) = g;
break break
end end
end end
case 1% Beta distribution (TODO: generalized beta distribution) case 1% Beta distribution (TODO: generalized beta distribution)
while condition while condition
y1 = gamma_draw(a(i),1,0); y1 = gamma_draw(a(i),1,0);
y2 = gamma_draw(b(i),1,0); y2 = gamma_draw(b(i),1,0);
tmp = y1/(y1+y2); tmp = y1/(y1+y2);
if tmp >= bounds(i,1) && tmp <= bounds(i,2) if tmp >= bounds(i,1) && tmp <= bounds(i,2)
pdraw(i) = tmp; pdraw(i) = pmean(i)+tmp*pstd(i);
break break
end end
end end
case 4% INV-GAMMA1 distribution case 4% INV-GAMMA1 distribution
while condition
tmp = sqrt(1/gamma_draw(p2(i)/2,1/p1(i),0));
if tmp >= bounds(i,1) && tmp <= bounds(i,2)
pdraw(i) = tmp;
break
end
end
case 6% INV-GAMMA2 distribution case 6% INV-GAMMA2 distribution
while condition
tmp = 1/gamma_draw(p2(i)/2,1/p1(i),0);
if tmp >= bounds(i,1) && tmp <= bounds(i,2)
pdraw(i) = tmp;
break
end
end
otherwise otherwise
disp('prior_draw:: Error!') % Nothing to do here.
disp('Unknown prior distribution.')
pdraw(i) = NaN;
end end
end
end
function gamma_draw(a,b,c) function gamma_draw(a,b,c)
% Bauwens, Lubrano & Richard (page 316) % Bauwens, Lubrano & Richard (page 316)
if a >30 if a >30
z = randn; z = randn;
g = b*(z+sqrt(4*a-1))^2/4 + c; g = b*(z+sqrt(4*a-1))^2/4 + c;
else else
x = -1; condi = 1
while x<0 while condi
u1 = rand; x = -1;
y = tan(pi*u1); while x<0
x = y*sqrt(2*a-1)+a-1; u1 = rand;
end y = tan(pi*u1);
while condition x = y*sqrt(2*a-1)+a-1;
u2 = rand; end
if log(u2) <= log(1+y^2)+(a-1)*log(x/(a-1))-y*sqrt(2*a-1); u2 = rand;
break if log(u2) <= log(1+y^2)+(a-1)*log(x/(a-1))-y*sqrt(2*a-1);
end break
end
end end
g = x*b+c; g = x*b+c;
end end