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-bf33cf982152time-shift
adjemian
parent
e66f3884db
commit
22e0f21220
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@ -2,13 +2,16 @@ function pdraw = prior_draw(init,cc)
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% Build one draw from the prior distribution.
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%
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% INPUTS
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% o SampleSize [integer] Size of the sample to build
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%
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% o init [integer] scalar equal to 1 (first call) or 0.
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% o cc [double] two columns matrix (same as in
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% metropolis.m), constraints over the
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% parameter space (upper and lower bounds).
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%
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% OUTPUTS
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% None.
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% o pdraw [double] draw from the joint prior density.
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%
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% ALGORITHM
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% None.
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% ...
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%
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% SPECIAL REQUIREMENTS
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% None.
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@ -16,10 +19,10 @@ function pdraw = prior_draw(init,cc)
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%
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% part of DYNARE, copyright S. Adjemian, M. Juillard (2006)
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% Gnu Public License.
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global M_ options_ estim_params_
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persistent fname npar bounds pshape pmean pstd a b p3 p4
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global M_ options_ estim_params_
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persistent fname npar bounds pshape pmean pstd a b p3 p4 condition
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if init
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if init
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nvx = estim_params_.nvx;
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nvn = estim_params_.nvn;
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ncx = estim_params_.ncx;
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@ -38,95 +41,121 @@ function pdraw = prior_draw(init,cc)
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a = zeros(npar,1);
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b = zeros(npar,1);
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if nargin == 2
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bounds = cc;
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bounds = cc;
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else
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bounds = [-Inf Inf];
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bounds = [-Inf Inf];
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end
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for i = 1:npar
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if pshape(i) == 3
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b(i) = pstd(i)^2/(pmean(i)-p3(i));
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a(i) = (pmean(i)-p3(i))/b(i);
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elseif pshape(i) == 1
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mu = (p1(i)-p3(i))/(p4(i)-p3(i));
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stdd = p2(i)/(p4(i)-p3(i));
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a(i) = (1-mu)*mu^2/stdd^2 - mu;
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b(i) = a*(1/mu - 1);
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elseif pshape(i) ==
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end
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pdraw = zeros(npar,1);
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switch pshape(i)
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case 3% Gaussian prior
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b(i) = pstd(i)^2/(pmean(i)-p3(i));
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a(i) = (pmean(i)-p3(i))/b(i);
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case 1% Beta prior
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mu = (p1(i)-p3(i))/(p4(i)-p3(i));
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stdd = p2(i)/(p4(i)-p3(i));
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a(i) = (1-mu)*mu^2/stdd^2 - mu;
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b(i) = a*(1/mu - 1);
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case 2;%Gamma prior
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mu = p1(i)-p3(i);
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b(i) = p2(i)^2/mu;
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a(i) = mu/b;
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case {5,4,6}
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% Nothing to do here
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%
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% 4: Inverse gamma, type 1, prior
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% p2(i) = nu
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% p1(i) = s
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% 6: Inverse gamma, type 2, prior
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% p2(i) = nu
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% p1(i) = s
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% 5: Uniform prior
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% p3(i) and p4(i) are used.
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otherwise
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disp('prior_draw :: Error!')
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disp('Unknown prior shape.')
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return
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end
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pdraw = zeros(npar,1);
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end
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condition = 1;
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pdraw = zeros(npar,1);
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return
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end
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for i = 1:npar
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end
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for i = 1:npar
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switch pshape(i)
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case 5% Uniform prior.
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pdraw(i) = rand*(p4(i)-p3(i)) + p3(i);
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case 3% Gaussian prior.
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while condition
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tmp = randn*pstd(i) + pmean(i);
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if tmp >= bounds(i,1) && tmp <= bounds(i,2)
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pdraw(i) = tmp;
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break
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end
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tmp = randn*pstd(i) + pmean(i);
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if tmp >= bounds(i,1) && tmp <= bounds(i,2)
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pdraw(i) = tmp;
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break
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end
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end
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case 2% Gamma prior.
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while condition
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g = gamma_draw(a(i),b(i),p3(i));
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if g >= bounds(i,1) && g <= bounds(i,2)
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pdraw(i) = g;
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break
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end
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end
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g = gamma_draw(a(i),b(i),p3(i));
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if g >= bounds(i,1) && g <= bounds(i,2)
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pdraw(i) = g;
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break
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end
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end
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case 1% Beta distribution (TODO: generalized beta distribution)
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while condition
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y1 = gamma_draw(a(i),1,0);
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y2 = gamma_draw(b(i),1,0);
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tmp = y1/(y1+y2);
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if tmp >= bounds(i,1) && tmp <= bounds(i,2)
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pdraw(i) = tmp;
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break
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end
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y1 = gamma_draw(a(i),1,0);
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y2 = gamma_draw(b(i),1,0);
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tmp = y1/(y1+y2);
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if tmp >= bounds(i,1) && tmp <= bounds(i,2)
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pdraw(i) = pmean(i)+tmp*pstd(i);
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break
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end
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end
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case 4% INV-GAMMA1 distribution
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while condition
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tmp = sqrt(1/gamma_draw(p2(i)/2,1/p1(i),0));
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if tmp >= bounds(i,1) && tmp <= bounds(i,2)
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pdraw(i) = tmp;
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break
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end
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end
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case 6% INV-GAMMA2 distribution
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while condition
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tmp = 1/gamma_draw(p2(i)/2,1/p1(i),0);
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if tmp >= bounds(i,1) && tmp <= bounds(i,2)
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pdraw(i) = tmp;
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break
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end
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end
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otherwise
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disp('prior_draw:: Error!')
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disp('Unknown prior distribution.')
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pdraw(i) = NaN;
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% Nothing to do here.
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end
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end
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end
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function gamma_draw(a,b,c)
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% Bauwens, Lubrano & Richard (page 316)
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if a >30
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if a >30
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z = randn;
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g = b*(z+sqrt(4*a-1))^2/4 + c;
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else
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x = -1;
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while x<0
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u1 = rand;
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y = tan(pi*u1);
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x = y*sqrt(2*a-1)+a-1;
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end
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while condition
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u2 = rand;
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if log(u2) <= log(1+y^2)+(a-1)*log(x/(a-1))-y*sqrt(2*a-1);
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break
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end
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else
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condi = 1
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while condi
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x = -1;
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while x<0
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u1 = rand;
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y = tan(pi*u1);
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x = y*sqrt(2*a-1)+a-1;
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end
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u2 = rand;
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if log(u2) <= log(1+y^2)+(a-1)*log(x/(a-1))-y*sqrt(2*a-1);
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break
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end
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end
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g = x*b+c;
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end
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end
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