The Influence of Conditional Distributions on Discovered Stochastic Differential Equation Models
Keywords: differential equation discovery, stochastic differential equations, conditional distribution
TL;DR: We could use simpler models, the conditional distribution has less order than marginal one
Abstract: In today’s rapidly advancing machine learning landscape, evaluating algorithm performance is paramount. As models grow more intricate, incorporating advanced mathematical frameworks like stochastic differential equations (SDEs) becomes essential for refining training processes. While SDEs inherently capture uncertainty and randomness, they also enhance predictive capabilities. This paper investigates the influence of conditional distributions within SDEs, examining how they affect the efficiency and precision of machine learning algorithms. Our analysis reveals promising avenues for developing novel strategies that could further optimize algorithm performance.
Submission Number: 34
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