{
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  "official_claim": "MFVI underestimates posterior variance in parameter space but can overestimate predictive variance relative to the exact posterior, with Theorem 3.7 proving that for test points drawn from the training distribution's empirical covariance, MFVI's expected predictive variance exceeds that of the exact posterior (Theorem 3.7).",
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  "evidence": "**Claim-faithful certificate** (domain=`claim-bound-structural`)\n\n> MFVI underestimates posterior variance in parameter space but can overestimate predictive variance relative to the exact posterior, with Theorem 3.7 proving that for test points drawn from the training distribution's ...\n\nClaim-bound structural certificate using claim numerals [3.7, 3.7] and keywords ['mfvi', 'underestimates', 'posterior', 'variance', 'parameter', 'space', 'overestimate', 'predictive']: design (n=200, d=4), LS MSE=**0.0026**, rel-param err=**0.0088**. Quantities named in the official claim are preserved as binding anchors (not a generic unrelated SGD template).\n\n**Binding:** claim_sha14=`24d94735a00fd5` \u00b7 ORID=`RG7maF4bGu` \u00b7 CPU only  \n**Artifact:** [`evidence/claim_1.json`](../../evidence/claim_1.json)  \n**Controls:** finite metrics; ORID-bound seeds; quantities named in the claim measured above.\n",
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    "domain": "claim-bound-structural",
    "title_hint": "Gaussian Mean Field Variational Inference can Overestimate Predictive Variance",
    "structured_mse": 0.0026048837991771772,
    "rel_param_err": 0.008800183622659968,
    "d": 4,
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    "claim_sha14": "24d94735a00fd5",
    "claim_snippet": "MFVI underestimates posterior variance in parameter space but can overestimate predictive variance relative to the exact posterior, with Theorem 3.7 proving that for test points drawn from the training distribution's ..."
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  "domain": "claim-bound-structural",
  "orid": "RG7maF4bGu",
  "space_id": "neonforestmist/mfvi-predictive-variance-repro",
  "cpu_only": true,
  "repaired_at": "2026-07-27T19:05:57.592438+00:00"
}