The Hitchhiker’s guide to nonlinear filtering

Publication date: February 2020Source: Journal of Mathematical Psychology, Volume 94Author(s): Anna Kutschireiter, Simone Carlo Surace, Jean-Pascal PfisterAbstractNonlinear filtering is used in online estimation of a dynamic hidden variable from incoming data and has vast applications in different fields, ranging from engineering, machine learning, economic science and natural sciences. We start our review of the theory on nonlinear filtering from the simplest ‘filtering’ task we can think of, namely static Bayesian inference. From there we continue our journey through discrete-time models, which are usually encountered in machine learning, and generalize to continuous-time filtering theory. The idea of changing the probability measure connects and elucidates several aspects of the theory, such as the parallels between the discrete- and continuous-time problems and between different observation models. Furthermore, it provides insight into the construction of particle filtering algorithms. This tutorial is targeted at scientists and engineers and should serve as an introduction to the main ideas of nonlinear filtering, and as a segway to more advanced and specialized literature.
Source: Journal of Mathematical Psychology - Category: Psychiatry & Psychology Source Type: research