LAUSR

Nonlinear filtering is the problem of 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 most simple filtering task we can think of, namely static Bayesian inference. From there we continue our journey through discrete-time models, which is usually encountered in machine learning, and generalize to and further emphasize continuous-time filtering theory. The idea of changing the probability measure connects and elucidates several aspects of the theory, such as the similarities between the discrete and continuous time nonlinear filtering equations, as well as formulations of these for different observation models. Furthermore, it gives insight into the construction of particle filtering algorithms. This tutorial is targeted at researchers in machine learning, time series analysis, and the natural sciences, and should serve as an introduction to the main ideas of nonlinear filtering, and as a segway to more specialized literature.