Investigation for #46 and #47, before touching either.
A scored history is linear-Gaussian, so its true joint posterior has a
closed form and the crate can be checked against ground truth. Measured
on five competitors:
means marginal sd (crate / exact)
tree (star) exact 1.000
loopy (robin) exact 0.502
On a tree the crate is exact in both. With cycles the means stay exact —
the standard Gaussian-BP result, and the property ratings rely on —
while marginal variances come out about half the true width.
That is the opposite direction from what #47 reports, so whatever is
happening in that consumer's model, the crate being conservative is not
it.
It also means #46 cannot be implemented as an added covariance accessor.
The exact correlation between two nodes here is +0.857, so ignoring it
overstates the width of a difference — but the too-narrow marginals
partially cancel that, leaving 1.327x rather than 2.646x. Adding true
correlations to these marginals without correcting them would give 0.765
against a true 1.524: overconfident, which is the direction the reporter
specifically called unsafe.
Pins the two real invariants (exactness on a tree, exact means with
cycles) and deliberately only records the variance gap, since closing it
is what #46 proposes.
Also records the working rules this project has converged on: investigate
before implementing, fix the root issue, and scout crates.io on measured
accuracy rather than adoption.
Refs #46, #47
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_011hcFjNDmHXZF8URGLku5zZ
