The Snaer program calculates the posterior mean and variance of variables on some of which we have data (with precisions), on some we have prior information (with precisions), and on some prior indicator ratios (with precisions) are available. The variables must satisfy a number of exact restrictions. The system is both large and sparse. Two aspects of the statistical and computational development are a practical procedure for solving a linear integer system, and a stable linearization routine for ratios. The numerical method for solving large sparse linear least-squares estimation problems is tested and found to perform well, even when the n×k design matrix is large (nk=O(108)).
Danilov, D., & Magnus, J. R. (2008). On the estimation of a large sparse Bayesian system : The Snaer program. Computational Statistics and Data Analysis, 52(9), 4203-4224. https://doi.org/10.1016/j.csda.2008.02.019