A variational approach for the online dual estimation of spatiotemporal systems governed by the IDE

Abstract A dual variational Bayes filter for states and parameter estimation in IDE based spatiotemporal dynamic systems is developed. Recursive updates are obtained from a restricted variational Bayesian perspective, using a dual filtering formulation where parameters are allowed to evolve in time. The added benefit over conventional point estimate filters is that parameter distributions are readily available for one to take advantage of in the design of complex experiments or in adaptive control scenarios. The dual filter is evaluated in a simulation study and seen to perform favorably when compared to a standard SMC approach.