State Tracking in the Presence of Heavy-tailed Observations

Yaman Kindap

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Paper

State Tracking in the Presence of Heavy-tailed Observations

Topics: Bayesian Models and Stochastic Methods; Classification and Clustering; Signal Processing

In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods ICPRAM - Volume 1, 135-142, 2021

Author: Yaman Kindap

Affiliation: Department of Computer Engineering, Bogazici University, Bebek, Istanbul, Turkey

Keyword(s): Signal Processing, Bayesian Models, Stochastic Methods, Classification, Clustering.

Abstract: In this paper, we define a state-space model with discrete latent states and a multivariate heavy-tailed observation density for applications in tracking the state of a system with observations including extreme deviations from the median. We use a Gaussian distribution with an unknown variance parameter which has a Gamma distribution prior depending on the state of the system to model the observation density. The key contribution of the paper is the theoretical formulation of such a state-space model which makes use of scale mixtures of Gaussians to yield an exact inference method. We derive the framework for estimation of the states and how to estimate the parameters of the model. We demonstrate the performance of the model on synthetically generated data sets.

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Paper citation in several formats:

Kindap, Y. (2021). State Tracking in the Presence of Heavy-tailed Observations. In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-486-2; ISSN 2184-4313, SciTePress, pages 135-142. DOI: 10.5220/0010150601350142

@conference{icpram21,
author={Yaman Kindap.},
title={State Tracking in the Presence of Heavy-tailed Observations},
booktitle={Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2021},
pages={135-142},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010150601350142},
isbn={978-989-758-486-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - State Tracking in the Presence of Heavy-tailed Observations
SN - 978-989-758-486-2
IS - 2184-4313
AU - Kindap, Y.
PY - 2021
SP - 135
EP - 142
DO - 10.5220/0010150601350142
PB - SciTePress