Forgetting of the initial distribution for Hidden Markov Models
统计理论
2008-07-18 v1 统计理论
摘要
The forgetting of the initial distribution for discrete Hidden Markov Models (HMM) is addressed: a new set of conditions is proposed, to establish the forgetting property of the filter, at a polynomial and geometric rate. Both a pathwise-type convergence of the total variation distance of the filter started from two different initial distributions, and a convergence in expectation are considered. The results are illustrated using different HMM of interest: the dynamic tobit model, the non-linear state space model and the stochastic volatility model.
引用
@article{arxiv.math/0703836,
title = {Forgetting of the initial distribution for Hidden Markov Models},
author = {Randal Douc and Gersende Fort and Eric Moulines and Pierre Priouret},
journal= {arXiv preprint arXiv:math/0703836},
year = {2008}
}