Matrix-noise Jacobians in stochastic-calculus inference and optimal paths

Abstract: Multiplicative noise makes stochastic dynamics depend on how the white-noise limit is interpreted. In multidimensional systems with matrix-valued noise amplitudes $σ(x)$, this dependence includes a local Jacobian contribution that is absent from the scalar examples most often used to build intuition. We formulate a finite-step path-likelihood framework for $θ$-discretized diffusions and show that its short-time expansion isolates the scalar $J_σ=\partial_jσ{ik}\partial_iσ{jk}-(\partial_iσ{ik})(\partial_lσ{lk})$. For a specified noise-amplitude representation $σ$, this quantity vanishes in one-dimensional, scalar-isotropic, and strictly diagonal cases, but can survive when state-dependent noise directions mix different components. We then test its consequences using paired comparisons that hold the drift, diffusion matrix, interpolation point, and Gaussian increment term fixed. In Model A, removing only the off-diagonal determinant contribution produces a shift in the fitted stochastic prescription that vanishes when $J_σ=0$. In Model B, removing the corresponding state-dependent action term changes a stable optimized transition path. These results show that a genuinely matrix-noise part of the short-time path measure can survive the scalar cancellations familiar from simpler settings and produce measurable changes in fitted stochastic prescriptions and Onsager--Machlup paths.