Correcting Boundary Bias and Observation Independence in Bayesian Experimental Design
A reconstruction-driven design density is derived and the posterior mean is used to build a training-free warp that places more measurements where the target function varies rapidly, and a geometric equalizer separately corrects boundary bias.
Sanna Jarl, Jens Sjölund, Jonathan J. S. Scragg et al.
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