learning_curve returns post-convergence posteriors, so every point is
smoothed: the estimate at a given date incorporates rounds played years
later. On ustat's data that starts six players' curves already spread apart
at sigma 0.9-1.6 against a prior of 6.0, barely moving thereafter.
filtered_learning_curve plots the same competitor on forward-only
information, so everyone starts at the prior and fans out. It could not be
reconstructed from the public API before: a caller could only refit over
events[0..k] for every k, which is O(n^2) fits for something one forward
pass already computes.
logaritmisk
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