What question did this study set out to answer?

To present a conditional entropy theorem related to mixture models and demonstrate its applications through examples.

February 19, 2026Open Access

Contextual Entropy Reduction in Mixture Models (Hanners Theorem): A Conditional Theorem, Proof, and Worked Example

Puntos clave

To present a conditional entropy theorem related to mixture models and demonstrate its applications through examples.
Designed a theoretical framework for contextual entropy reduction in mixture predictions.
Established conditions for strict inequality between entropies based on information characteristics.
Provided a worked example illustrating the theorem with finite-alphabet parameters.
Presented a clear framework showing that E_Psi[H(Q_psi)] <= H(X) holds under specific conditions.
Demonstrated the strict inequality occurs when I(X;Psi) > 0.
Developed a hierarchical-context parameterization system for practical applications.

Resumen

Preprint submitted to Communications in Mathematical Physics. This record contains the manuscript "Contextual Entropy Reduction in Mixture Models (Hanners Theorem): A Conditional Theorem, Proof, and Worked Example. " The paper presents a focused information-theoretic result for context-conditioned mixture prediction: EPsiH (Qₚsi) = H (X|Psi) 0. The manuscript explicitly states that the entropy decomposition is classical and contributes a clear mixture-model framing, strictness and equality diagnostics, a practical hierarchical-context parameterization, and a reproducible finite-alphabet worked example with expectation-level verification. Claims are intentionally limited to this conditional entropy theorem and its assumptions; broader complexity, quantum, and physics claims are not made. This version supersedes prior versions in this concept DOI and focuses on contextual entropy reduction in mixture models.

Preguntar a la IA

Me gusta

Guardar

Ver artículo completo