where a is a parameter that scales the regularization constraint (Louis 1989; Press et al. 1992) . Again, (7) has an unambiguous best-fit solution that can be determined algebraically with NNLS for any value of a, and the latter can be adjusted in a simple one-dimensional search such that the least-squares fit remains at a statistically indistinguishable quality compared with the initial best fit in the absence of regularization (Bevington and Robinson 1992). A Bayesian variation of this approach is possible that modulates the regularization matrix to enhance the information content of the solution in view of existing (or hypothesized) prior knowledge (Sivia 1996; Brown et al. 2007; Patel et al. 2008 ).