Summary
This talk aims to introduce a statistical approach to identify the stochastic dynamic system behind a measured time series data. The method is formulated as a maximum likelihood problem, where we estimate the parameters of the dynamical system generating the data by solving the corresponding optimisation problem. This flexible formulation allows the identification of stochastic dynamical systems even through noisy measurements. We demonstrate the method on synthetic datasets generated by stochastic nonlinear oscillators and investigate statistical properties of the estimators through numerical experiments.
