Given U, estimate π by maximum likelihood from the condition-by-condition results. This step can rescue imperfections in Step 1a by assigning low weight to covariance matrices that are not well-supported by the data. This step also adapts to sparse effects; if most effects are zero, or small, this step will put most weight on small effects (i.e., small scaling coefficients,ω).
