 	// Here we condition on sigma_popn_true (for simple analytical result).
 	// Currently ineffecient; should build host vector and copy over.
 	int dim_theta = 2;
-	int n_theta = 10;
-	double mu_lo = mu_popn_true - 5*sigma_msmt/10; //sqrt(n);
-	double mu_hi = mu_popn_true + 5*sigma_msmt/10; //sqrt(n);
+	int n_theta = 11;
+	double mu_lo = mu_popn_true - 1*sigma_msmt/10; //sqrt(n);
+	double mu_hi = mu_popn_true + 1*sigma_msmt/10; //sqrt(n);
 	double dmu = (mu_hi - mu_lo)/(n_theta-1.);
 	double mu;
 	thrust::host_vector<double> h_theta(dim_theta*n_theta);

int main(void)
{
	// measurements
	int n = 10000000; // # of items
	int m = 1; // # of features
	/*
	wrapvec d_features(m,n);

			int end = start + n;
			double log_marg = 0;
			log_marg = thrust::reduce(d_marg.begin()+start, d_marg.begin()+end);
			//std::cout << i << " " << log_marg << std::endl;
            printf("%d %20.10f %20.10f \n", i, log_marg, h_theta[i*dim_theta]);
		}

	}
