Finally, the application of the Monte Carlo approach to achieve greater parsimony of the results (i.e., simplicity of the distribution in the sense of suppressing artificial detail) is equally novel, but not very successful when we applied this idea to our example data analysis. An example of the lack of regularization in the 2DSA method resulting in artificial detail can be found in the data shown by Planken et al. (2008) , where a standard c(s) analysis of SV data with maximum-entropy regularization exhibits only a single broad skewed distribution [ Fig. 3c in Planken et al. (2008) ], consistent with the expected continuous size distribution of the material studied, yet the 2DSA analysis of the same data suggests the presence of more than 14 discrete peaks (at different s-values and all at similar frictional ratio) [ Fig. 4 in Planken et al. (2008) ]. The Monte Carlo approach is certainly an extremely computationally costly step, in particular if one would carry it out with statistically meaningful iteration numbers. In contrast, application of the standard Tikhonov regularization to the full highresolution problem, with or without Bayesian modulation, takes a small fraction of the computational effort of the original problem, i.e., on the order of seconds on a PC.