A Particle Filter Method of Inference for Stochastic Differential Equations

Abstract

Stochastic Differential Equations (SDE) serve as an extremely useful modelling tool in areas including ecology, finance, population dynamics, and physics. Yet, parameter inference This thesis explores this latter approach. Specifically, we use a particle filter tailored to SDE models and consider various methods for approximating the gradient and hessian of the parameter log-posterior. Empirical results for several SDE models are presented.