A matrix-noise Jacobian J_σ = ∂_j σ_ik ∂_i σ_jk − (∂_i σ_ik)(∂_l σ_lk) survives scalar cancellations and measurably affects path likelihoods and Onsager-Machlup paths in multidimensional systems.
Arnoulx de Pirey, L
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Matrix-noise Jacobians in stochastic-calculus inference and optimal paths
A matrix-noise Jacobian J_σ = ∂_j σ_ik ∂_i σ_jk − (∂_i σ_ik)(∂_l σ_lk) survives scalar cancellations and measurably affects path likelihoods and Onsager-Machlup paths in multidimensional systems.