Combining variational data assimilation and particle filters: the variational mapping particle filter

Abstract

Recent works in the machine learning community have started to combine two classical statistical concepts: Monte Carlo sampling and variational inference. In the traditional variational inference, including variational data assimilation, some parameters of a proposed posterior density are estimated through maximazing the marginal likelihood or via maximum a posteriori estimation.