Weak local linear discretizations for stochastic differential equations: Convergence and numerical schemes

Weak local linear (WLL) discretizations are playing an increasing role in the construction of effective numerical integrators and inference methods for stochastic differential equations (SDEs) with additive noise. However, due to limitations in the existing numerical implementations of WLL discretizations, the resulting integrators and inference methods have either been restricted to particular classes of autonomous SDEs or showed low computational efficiency. Another limitation is the absence of a systematic theoretical study of the rate of convergence of the WLL discretizations and numerical integratos. This task is the main purpose of the present paper. A second goal is introducing a new WLL scheme that overcomes the numerical limitations mentioned above. Additionally, a comparative analysis between the new WLL scheme and some conventional weak integrators is also presented.