We present a novel deep learning method for estimating time-dependent parameters in Markov processes through discrete sampling. Departing from conventional machine learning, our approach reframes parameter approximation as an optimization problem using the maximum likelihood approach. Experimental validation focuses on parameter estimation in multivariate regression and stochastic differential equations (SDEs). Theoretical results show that the real solution is close to SDE with parameters approximated using our neural network-derived under specific conditions. Our work contributes to SDE-based model parameter estimation, offering a versatile tool for diverse fields.

翻译：我们提出了一种新颖的深度学习方法，用于通过离散采样估计马尔可夫过程中的时间依赖参数。与传统的机器学习不同，我们的方法将参数近似重新表述为一个基于极大似然法的优化问题。实验验证主要集中在多元回归和随机微分方程中的参数估计。理论结果表明，在特定条件下，使用我们的神经网络推导出的近似参数所对应的随机微分方程解接近真实解。我们的工作推动了基于随机微分方程的模型参数估计，为多个领域提供了一种通用工具。