Stochastic Differential Equation Based on a Multimodal Potential to Model Movement Data in Ecology
提出一种基于多模态势能表面的随机微分方程模型，用于描述个体在生态学中的运动，并比较了多种参数推断方法，发现常用欧拉法效果较差。
Summary The paper proposes a new model for individuals’ movement in ecology. The movement process is defined as a solution to a stochastic differential equation whose drift is the gradient of a multimodal potential surface. This offers a new flexible approach among the popular potential-based movement models in ecology. To perform parameter inference, the widely used Euler method is compared with two other pseudolikelihood procedures and with a Monte Carlo expectation–maximization approach based on exact simulation of diffusions. Performances of all methods are assessed with simulated data and with a data set of fishing vessel trajectories. We show that the usual Euler method performs worse than the other procedures for all sampling schemes.