Parameter inference#
Estimating tissue properties from a measured volume.
Every estimator is made from the simulator it inverts and fitted over the same statement of the problem – what is unknown, over what range, at what noise level – and they differ only in how they fill it in. These examples change only that: dictionary matching over a parameter grid, compressed and clustered; interpolation along a curve where there is a single unknown; a nonlinear fit of the model itself; and a kernel regression that never builds a grid at all.
Every one of them maps the same BrainWeb slice, so the answer is known everywhere – mixtures of tissues included – and reports what it cost in time and in memory alongside what it got wrong.
