Innovation Method is a recognized method for the estimation of parameters in diffusion processes. It is well known that the quality of the Innovation Estimator strongly depends on an adequate selection of the initial value for the parameters when a local optimization algorithm is used in its computation. Alternatively, in this paper, we use a strategy based on a modern method for solving global optimization problems, Estimation of Distribution Algorithms (EDAs). We study the feasibility of a specific EDA - a continuous version of the Univariate Marginal Distribution Algorithm (UMDAc) - for the computation of the Innovation Estimators. Through numerical simulations, we show that the considered global optimization algorithms substantially improves the effectiveness of the Innovation Estimators for different types of diffusion processes with complex nonlinear and stochastic dynamics.

创新方法是公认的用于估计扩散过程中参数的方法。众所周知的是，创新估计的质量在很大程度上取决于这些参数的初始值的适当选择，当一个局部优化算法在其计算中被使用。另外，在本文中，我们使用一种基于现代方法的策略来解决全局优化问题，即分配算法的估计（EDA）。我们研究了特定的EDA（单变量边际分布算法（UMDAc）的连续版本）用于计算创新估计量的可行性。通过数值模拟