@article{ECP1423,
author = {Ramon Van Handel},
title = {Discrete time nonlinear filters with informative observations are stable},
journal = {Electron. Commun. Probab.},
fjournal = {Electronic Communications in Probability},
volume = {13},
year = {2008},
keywords = {nonlinear filtering; prediction; asymptotic stability; hidden Markov models},
abstract = {The nonlinear filter associated with the discrete time signal-observation model $(X_k,Y_k)$ is known to forget its initial condition as $k\to\infty$ regardless of the observation structure when the signal possesses sufficiently strong ergodic properties. Conversely, it stands to reason that if the observations are sufficiently informative, then the nonlinear filter should forget its initial condition regardless of any properties of the signal. We show that for observations of additive type $Y_k=h(X_k)+\xi_k$ with invertible observation function $h$ (under mild regularity assumptions on $h$ and on the distribution of the noise $\xi_k$), the filter is indeed stable in a weak sense without any assumptions at all on the signal process. If the signal satisfies a uniform continuity assumption, weak stability can be strengthened to stability in total variation.},
pages = {no. 53, 562-575},
issn = {1083-589X},
doi = {10.1214/ECP.v13-1423},
url = {http://ecp.ejpecp.org/article/view/1423}}