Work

Sensitivity of hidden Markov models

Year: 2005

Type: article

Abstract: We derive a tight perturbation bound for hidden Markov models. Using this bound, we show that, in many cases, the distribution of a hidden Markov model is considerably more sensitive to perturbations in the emission probabilities than to perturbations in the transition probability matrix and the initial distribution of the underlying Markov chain. Our approach can also be used to assess the sensitivity of other stochastic models, such as mixture processes and semi-Markov processes.

Source: Journal of Applied Probability

DOI: 10.1017/s002190020000067x

Authors: Alexander Yu. Mitrophanov, Alexandre Lomsadze, Mark Borodovsky

Institution: Georgia Institute of Technology

Language: English

Topic: Bayesian Methods and Mixture Models

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funder: National Institutes of Health

OpenAlex ID: W2108421005