Martingale Neural Operator uses Doob-Meyer factorization to output mean and low-rank covariance for stochastic PDE terminal laws, achieving large Wasserstein reductions versus diffusion baselines on tested SPDEs.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Bayesian joint estimation of Hurst parameter and volatility in fractional SDE models is developed to propagate parameter uncertainty into fractional Black-Scholes option prices.
Gamma-heterogeneous stopping rates applied to subdiffusive fBM yield heavy-tailed radial position densities while preserving localized aggregation.
citing papers explorer
-
Martingale Neural Operators: Learning Stochastic Marginals via Doob-Meyer Factorization
Martingale Neural Operator uses Doob-Meyer factorization to output mean and low-rank covariance for stochastic PDE terminal laws, achieving large Wasserstein reductions versus diffusion baselines on tested SPDEs.
-
Bayesian Joint Estimation of the Hurst Parameter and Volatility with Applications to Fractional Option Pricing
Bayesian joint estimation of Hurst parameter and volatility in fractional SDE models is developed to propagate parameter uncertainty into fractional Black-Scholes option prices.
-
Heavy-Tailed Dispersal Kernels from Stopped Subdiffusive Fractional Brownian Motion
Gamma-heterogeneous stopping rates applied to subdiffusive fBM yield heavy-tailed radial position densities while preserving localized aggregation.