On the Prediction of a Nonstationary Bernoulli Distribution based on Bayes Decision Theory

Daiki Koizumi

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 Harvard Style

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, pages 957-965. DOI: 10.5220/0010270709570965


in Bibtex Style

@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},
}


in EndNote Style

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
AU - Koizumi D.
PY - 2021
SP - 957
EP - 965
DO - 10.5220/0010270709570965