Supplementary Material S1 Pseudocode Algorithm 1 gives pseudocode for autofocusing a broad class of model-based optimization (MBO)

"E-step" (Steps 1 and 2 in Algorithm 1) and a weighted maximum likelihood estimation (MLE) "M-step" (Step 3; see [ ( t 1) (t 1) One may use these in a number of different ways. The following observation is due to Chebyshev's inequality. One can use Proposition S2.1 to construct a confidence interval on, for example, the expected squared Note that 1) the bound in Proposition S2.1 is CbAS naturally controls the importance weight variance. Design procedures that leverage a trust region can naturally bound the variance of the importance weights. We used CbAS as follows.