Associator
Objects and domain are specified in a normed algebra; this is a definition, not an experimental result.
SOURCE_ANCHORED · Section 2; 3.1 · PDF page 2
C3.8U · FORMULA WORKBENCH
Lower bounds for selected separations. Each formula links to its assumptions and a verified proof page.
| Bound | Value ≈ | Unit |
|---|---|---|
| ℛ_S | 1 | 1 |
| ℛ_R | 0.5 | 1 |
| ℛ_D | 0.75 | 1 |
| ℛ_Sℛ_D | 0.75 | 1 |
| ℛ_Rℛ_D | 0.375 | 1 |
| ℛ_Sℛ_D (c) | 0.375 | 1 |
The numerical condition Δ_S ≥ c‖K‖ holds. This does not prove separation throughout Ω.
Export includes inputs, exact fractions, units, formula references and evidence statuses.
Entering numbers does not validate a physical model. e_X is the maximum error of a fixed estimate; ℛ_X is the maximum expected metric loss. At τ = 0 the coefficients agree, but the definitions remain different.
Objects and domain are specified in a normed algebra; this is a definition, not an experimental result.
SOURCE_ANCHORED · Section 2; 3.1 · PDF page 2
Objects and domain are specified in a normed algebra; this is a definition, not an experimental result.
SOURCE_ANCHORED · Section 3.4 · PDF page 3
Theorem package: A1–A4, one estimator for hidden branches. A positive bound requires nonzero separations.
SOURCE_ANCHORED · Section 3.3 · PDF page 3
Additionally c > 0 and Δ_S(u) ≥ c‖K(u)‖. A numerical row does not prove separation over all of Ω.
SOURCE_ANCHORED · Section 3.5 · PDF page 3
Theorem package: A1–A4, one estimator for hidden branches. A positive bound requires nonzero separations.
SOURCE_ANCHORED · Section 7; 8 · PDF page 4
Theorem package: A1–A4, one estimator for hidden branches. A positive bound requires nonzero separations.
SOURCE_ANCHORED · Section 7; 8 · PDF page 4
Additionally c > 0 and Δ_S(u) ≥ c‖K(u)‖. A numerical row does not prove separation over all of Ω.
SOURCE_ANCHORED · Section 7; 8 · PDF page 5
Two probability laws on one measurable space; τ uses the entire accessible observation.
SOURCE_ANCHORED · Section 3 · PDF page 2
Two probability laws on one measurable space; τ uses the entire accessible observation.
SOURCE_ANCHORED · Section 3 · PDF page 3
C3.8T audit: A1–A6, common complete-data laws and one decision kernel; finite risks.
SOURCE_ANCHORED · Section 2 · PDF page 2
C3.8T audit: A1–A6, common complete-data laws and one decision kernel; finite risks.
SOURCE_ANCHORED · Section 4; 5 · PDF page 3
C3.8T audit: A1–A6, common complete-data laws and one decision kernel; finite risks.
SOURCE_ANCHORED · Section 4; 5 · PDF page 3
C3.8T audit: A1–A6, common complete-data laws and one decision kernel; finite risks.
SOURCE_ANCHORED · Section 4; 5 · PDF page 3
C3.8T audit: A1–A6, common complete-data laws and one decision kernel; finite risks.
SOURCE_ANCHORED · Section 5 · PDF page 4
C3.8T audit: A1–A6, common complete-data laws and one decision kernel; finite risks.
SOURCE_ANCHORED · Section 5 · PDF page 4
Additionally c > 0 and Δ_S(u) ≥ c‖K(u)‖. A numerical row does not prove separation over all of Ω.
SOURCE_ANCHORED · Section 5 · PDF page 4
Deterministic model: normed algebra, two defined bracket outcomes, hidden branch index, one estimate and Δ_D > 0. The c-form additionally requires proven c-separation.
Statistical model: the entire accessible observation Y, laws P_L/P_R, measurable metric loss, one branch-independent decision kernel and finite risks; K = r_L − r_R. The c-form requires separation.
The canonical statistical patch has not been applied. Physical validation has not been performed. truth_layer_promotion=0 · EXT_RUN=NOT_RUN · NO_LOSS_FREEZE=BLOCKED.