Relational Attribution of Model Preference

The change in model preference produced by a new dataset depends on the inference context in which it is evaluated. This preprint gives an auditable account of that dependence. An exact finite-update identity ΔG = D + R₁ − R₀ separates the incoming conditional likelihood evaluated at the previous fit from the gain due to reoptimization; a path integral of score–inverse-Hessian contractions describes the latter exactly, and a finite-face identity separates explicit nonadditivity from adaptation through shared parameters, with a Bayesian counterpart based on posterior covariance. Complete tables of coalition optima do not determine the update decomposition. In a shared-mean Gaussian model every higher-order Möbius interaction has an explicit nonzero sampling law and is blind to a common signal; the blindness extends to model-space signals in weighted linear regression with a fixed null. New in version 1.0: exact null laws for anchored data-combination faces K = G(DCX) − G(DC) − G(DX) + G(D). In nested linear-Gaussian models every signed coalition contrast is a generalized chi-square variable whose mean is the alternating sum of identified ranks; anchored faces with an identifying baseline have mean zero. In an orthogonal reference model the face is exactly √(f_C f_X)·(A − B) with A, B independent χ²_k, where f_C, f_X are the information fractions added by C and X; its law does not depend on the true values of the extra parameters, so the face is a consistency diagnostic, not signal evidence. The published DESI DR2 faces are then calibrated from public inputs: the DESI DR2 BAO data vector and full covariance, the DESI compressed CMB prior on (θ*, ω_b, ω_bc) (DESI DR2, App. A), and supernova Fisher surrogates calibrated to the published DESI+CMB+SN constraints. The exact null law gives standard deviations of 2.4–3.1; the observed contrasts −2.0 (Pantheon+), −0.5 (Union3) and −0.4 (DES-Y5) lie at 0.84–0.98σ, 0.30–0.43σ and 0.24–0.31σ across 30 surrogate variants per branch. The sign change of the Pantheon+ increment between the DESI and DESI+CMB contexts is statistically ordinary in the linear-Gaussian approximation. The work is related to, and distinguished from, the concordance estimators of Raveri & Hu (2019). No new cosmological fit, tension significance or dark-energy detection is claimed. The unchanged v0.1 audit (29 test groups) and a v1.0 extension audit are embedded in the PDF and uploaded separately.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-30
DOI
https://doi.org/10.5281/zenodo.23048061
Primary Topic
Gaussian Processes and Bayesian Inference
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
preprint

Relational Attribution of Model Preference

Oliver Tuma
Zenodo (CERN European Organization for Nuclear Research)
Gaussian Processes and Bayesian Inference
preprint

Relational Attribution of Model Preference

Oliver Tuma
preprint en

Abstract

The change in model preference produced by a new dataset depends on the inference context in which it is evaluated. This preprint gives an auditable account of that dependence. An exact finite-update identity ΔG = D + R₁ − R₀ separates the incoming conditional likelihood evaluated at the previous fit from the gain due to reoptimization; a path integral of score–inverse-Hessian contractions describes the latter exactly, and a finite-face identity separates explicit nonadditivity from adaptation through shared parameters, with a Bayesian counterpart based on posterior covariance. Complete tables of coalition optima do not determine the update decomposition. In a shared-mean Gaussian model every higher-order Möbius interaction has an explicit nonzero sampling law and is blind to a common signal; the blindness extends to model-space signals in weighted linear regression with a fixed null. New in version 1.0: exact null laws for anchored data-combination faces K = G(DCX) − G(DC) − G(DX) + G(D). In nested linear-Gaussian models every signed coalition contrast is a generalized chi-square variable whose mean is the alternating sum of identified ranks; anchored faces with an identifying baseline have mean zero. In an orthogonal reference model the face is exactly √(f_C f_X)·(A − B) with A, B independent χ²_k, where f_C, f_X are the information fractions added by C and X; its law does not depend on the true values of the extra parameters, so the face is a consistency diagnostic, not signal evidence. The published DESI DR2 faces are then calibrated from public inputs: the DESI DR2 BAO data vector and full covariance, the DESI compressed CMB prior on (θ*, ω_b, ω_bc) (DESI DR2, App. A), and supernova Fisher surrogates calibrated to the published DESI+CMB+SN constraints. The exact null law gives standard deviations of 2.4–3.1; the observed contrasts −2.0 (Pantheon+), −0.5 (Union3) and −0.4 (DES-Y5) lie at 0.84–0.98σ, 0.30–0.43σ and 0.24–0.31σ across 30 surrogate variants per branch. The sign change of the Pantheon+ increment between the DESI and DESI+CMB contexts is statistically ordinary in the linear-Gaussian approximation. The work is related to, and distinguished from, the concordance estimators of Raveri & Hu (2019). No new cosmological fit, tension significance or dark-energy detection is claimed. The unchanged v0.1 audit (29 test groups) and a v1.0 extension audit are embedded in the PDF and uploaded separately.

Zenodo (CERN European Organization for Nuclear Research)
Peace, Justice and strong institutions
Gaussian Processes and Bayesian Inference
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.