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.
Scores every event on what was known before it, rather than on priors that
carry information from events which had not happened yet. This is the
quantity HistoryBuilder::online promised and never delivered.
The pass walks slices in time order carrying its own forward messages, and
per slice runs the unmodified production sweep on a scratch copy whose
backward message is left improper. Reusing iterate_to_convergence rather
than reimplementing inference means a competitor playing twice at one time
is handled by the same within-slice EP that converge() uses, instead of
being approximated the way the old evidence paths approximated it.
Nothing is stored on Skill and nothing on self is mutated, so the result is
independent of whether converge() has run — the property a stored field
cannot have.
logaritmisk