Research PaperResearchia:202601.12e33631
A Complete Decomposition of Stochastic Differential Equations
Samuel Duffield
Abstract
We show that any stochastic differential equation with prescribed time-dependent marginal distributions admits a decomposition into three components: a unique scalar field governing marginal evolution, a symmetric positive-semidefinite diffusion matrix field and a skew-symmetric matrix field.
Submitted: January 12, 2026Subjects: Machine Learning; Machine Learning
Description / Details
We show that any stochastic differential equation with prescribed time-dependent marginal distributions admits a decomposition into three components: a unique scalar field governing marginal evolution, a symmetric positive-semidefinite diffusion matrix field and a skew-symmetric matrix field.
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Submission Info
Date:
Jan 12, 2026
Jan 12, 2026
Topic:
Machine Learning
Machine Learning
Area:
Machine Learning
Machine Learning
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