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# inverse gaussian distribution NaN values

5 ビュー (過去 30 日間)
Rini 2015 年 3 月 3 日
Hello,
I am working on a non-negative Bayesian Lasso model (<http://arxiv.org/pdf/1009.2300v1.pdf>) and I am sampling values from Inverse Gaussian distribution. Since I want only positive coefficients, I am using first 'mvnrnd' to generate the coefficients and then setting the negative values to 0. This is causing problem in the next steps where I use
randraw('gig',[-1/2,x,y],1);
with (e.g.) x 0.226 and y as 2.26e-19. with these parameters 'randraw' returns NaN values. The code for sampling inverse Gaussian is provided below-
Mu_tau = lambda2.*sqrt(Sigma2)./Beta;
Mu_tau(Mu_tau>1e10)=1e9;
Lambda_tau = lambda2.^2;
A_tau = Lambda_tau./(Mu_tau.^2);
tau2_temp = zeros(M,1);
for m = 1 : M
tau2_temp(m,1) = randraw('gig',[-1/2,Lambda_tau(m),A_tau(m)],1);
end
tau2 = (1./tau2_temp)';
where Beta is the non negative coefficient, M is the number of regressors and lambda is the LASSO penalizing vector. I have found other papers on Bayesian Lasso which works fine for me if I remove the non negative constraint. But with the constraint I always have this problem. Can someone help me? Sorry if my question is too long.
Thanks

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