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Author: Daiki Koizumi

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

Keyword(s): Probability Model, Bayes Decision Theory, Nonstationary Bernoulli Distribution, Hierarchical Bayesian Model.

Abstract: A class of nonstationary Bernoulli distribution is considered in terms of Bayes decision theory. In this nonstationary class, the Bernoulli distribution parameter follows a random walking rule. Even if this general class is assumed, it is proved that the posterior distribution of the parameter can be obtained analytically with a known hyper parameter. With this theorem, the Bayes optimal prediction algorithm is proposed assuming the 0-1 loss function. Using real binary data, the predictive performance of the proposed model is evaluated comparing to that of a stationary Bernoulli model.

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Paper citation in several formats:
Koizumi, D. (2021). On the Prediction of a Nonstationary Bernoulli Distribution based on Bayes Decision Theory. In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-484-8; ISSN 2184-433X, SciTePress, pages 957-965. DOI: 10.5220/0010270709570965

@conference{icaart21,
author={Daiki Koizumi.},
title={On the Prediction of a Nonstationary Bernoulli Distribution based on Bayes Decision Theory},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2021},
pages={957-965},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010270709570965},
isbn={978-989-758-484-8},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - On the Prediction of a Nonstationary Bernoulli Distribution based on Bayes Decision Theory
SN - 978-989-758-484-8
IS - 2184-433X
AU - Koizumi, D.
PY - 2021
SP - 957
EP - 965
DO - 10.5220/0010270709570965
PB - SciTePress

- Science and Technology Publications, Lda.
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