We present a new strategy to approximate the global solution of the Fokker-Planck equation efficiently in higher dimensions and show its convergence. The main ingredients are the Euler scheme to solve the associated stochastic differential equation and a histogram method for tree-structured density estimation on a data-dependent partitioning of the state space R^d.

翻译：我们提出一种新策略，用于在高维空间中高效逼近福克-普朗克方程的整体解，并证明其收敛性。其主要要素包括求解相关随机微分方程的欧拉格式，以及基于数据依赖划分的状态空间R^d上树结构密度估计的直方图方法。