inverse gaussian distribution NaN values

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