Uncertainty and filtering of hidden Markov models in discrete time
Methodology
2018-05-15 v4 Optimization and Control
Probability
Abstract
We consider the problem of filtering an unseen Markov chain from noisy observations, in the presence of uncertainty regarding the parameters of the processes involved. Using the theory of nonlinear expectations, we describe the uncertainty in terms of a penalty function, which can be propagated forward in time in the place of the filter.
Keywords
Cite
@article{arxiv.1606.00229,
title = {Uncertainty and filtering of hidden Markov models in discrete time},
author = {Samuel N. Cohen},
journal= {arXiv preprint arXiv:1606.00229},
year = {2018}
}