Nonlinear filtering is a central mathematical tool in understanding how we process information. Sadly, the equations involved are often very high dimensional, which may lead to difficulties in applications. One possible resolution is to replace the filter by a low-dimensional approximation, with hopefully small error. In this talk we will see how, in the case where the underlying process is a finite-state Markov Chain, now-classical results on the stability of filters can be strengthened to show that this introduces a well-controlled error, leveraging tools from information geometry.

(Based on joint work with Eliana Fausti)