On estimation states of hidden markov models in condition of unknown transition matrix
Probability
2015-03-10 v2 Statistics Theory
Statistics Theory
Abstract
In this paper, we develop methods of nonlinear filtering and prediction of an unobservable Markov chain with a finite set of states. This Markov chain controls coefficients of AR(p) model. Using observations generated by AR(p) model we have to estimate the state of Markov chain in the case of an unknown probability transition matrix. Comparison of proposed non-parametric algorithms with the optimal methods in the case of the known transition matrix is carried out by simulating.
Keywords
Cite
@article{arxiv.1503.00167,
title = {On estimation states of hidden markov models in condition of unknown transition matrix},
author = {Vasily Vasilyev and Alexander Dobrovidov},
journal= {arXiv preprint arXiv:1503.00167},
year = {2015}
}