Credible Interval Prediction of a Nonstationary Poisson Distribution based on Bayes Decision Theory

Daiki Koizumi

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Paper

Credible Interval Prediction of a Nonstationary Poisson Distribution based on Bayes Decision Theory

Topics: Data Mining; Data Science; Machine Learning

In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, 995-1002, 2020 , Valletta, Malta

Author: Daiki Koizumi

Affiliation: Otaru University of Commerce, 3–5–21, Midori, Otaru-city, Hokkaido, 045–8501, Japan

Keyword(s): Bayes Decision Theory, Credible Interval, Nonstationary Poisson Distribution.

Abstract: A credible interval prediction problem of a nonstationary Poisson distribution in terms of Bayes decision theory is considered. This is the two-dimensional optimization problem of the Bayes risk function with respect to two variables: upper and lower limits of credible interval prediction. We prove that these limits can be uniquely obtained as the upper or lower percentile points of the predictive distribution under a certain loss function. By applying this approach, the Bayes optimal prediction algorithm for the credible interval is proposed. Using real web traffic data, the performance of the proposed algorithm is evaluated by comparison with the stationary Poisson distribution.

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Paper citation in several formats:

Koizumi, D. (2020). Credible Interval Prediction of a Nonstationary Poisson Distribution based on Bayes Decision Theory. In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-395-7; ISSN 2184-433X, SciTePress, pages 995-1002. DOI: 10.5220/0009182209951002

@conference{icaart20,
author={Daiki Koizumi.},
title={Credible Interval Prediction of a Nonstationary Poisson Distribution based on Bayes Decision Theory},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2020},
pages={995-1002},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009182209951002},
isbn={978-989-758-395-7},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Credible Interval Prediction of a Nonstationary Poisson Distribution based on Bayes Decision Theory
SN - 978-989-758-395-7
IS - 2184-433X
AU - Koizumi, D.
PY - 2020
SP - 995
EP - 1002
DO - 10.5220/0009182209951002
PB - SciTePress