Uncertainty Quantification
Cross-source consensus on Uncertainty Quantification from 1 sources and 4 claims.
1 sources · 4 claims
Uses
How it works
Risks & contraindications
Highlighted claims
- The low-rank covariance factorization B^T B automatically enforces positive semidefiniteness without eigenvalue clipping or Gram-Schmidt steps. — Martingale Neural Operators: Learning Stochastic Marginals via Doob-Meyer Factorization
- The Gaussian residual law assumed by MNO cannot represent heavy tails, jumps, or multimodal terminal distributions. — Martingale Neural Operators: Learning Stochastic Marginals via Doob-Meyer Factorization
- MNO fills a niche for one-shot terminal marginal moments at operator speed that existing approaches cannot fill without significant computational overhead. — Martingale Neural Operators: Learning Stochastic Marginals via Doob-Meyer Factorization
- MNO's diagonal variance consistency loss does not penalize off-diagonal covariance entries, resulting in worse full-covariance recovery than Neural SPDE despite better mean accuracy. — Martingale Neural Operators: Learning Stochastic Marginals via Doob-Meyer Factorization