Convergence in distribution for filtering processes associated to Hidden Markov Models with densities
Probability
2016-06-03 v3
Abstract
Consider a filtering process associated to a hidden Markov model with densities for which both the state space and the observation space are complete, separable, metric spaces. If the underlying, hidden Markov chain is strongly ergodic and the filtering process fulfills a certain coupling condition we prove that, in the limit, the distribution of the filtering process is independent of the initial distribution of the hidden Markov chain. If furthermore the hidden Markov chain is uniformly ergodic, then we prove that the filtering process converges in distribution.
Cite
@article{arxiv.1305.5797,
title = {Convergence in distribution for filtering processes associated to Hidden Markov Models with densities},
author = {Thomas Kaijser},
journal= {arXiv preprint arXiv:1305.5797},
year = {2016}
}
Comments
54 pages revision. Rewritten introduction. Theorem 12.1 sharper than Theorem 16.1 (v1). Proofs and results reorganised. Example 18.3 (v1) excluded