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Modeling of mixed data for Poisson prediction
- 1.0524975 - ÚTIA 2021 RIV US eng C - Conference Paper (international conference)
Petrouš, Matej - Uglickich, Evženie
Modeling of mixed data for Poisson prediction.
Applied Computational Intelligence and Informatics (SACI) : 2020 IEEE 14th International Symposium on Applied Computational Intelligence and Informatics (SACI). Piscataway: IEEE, 2020, s. 77-82. ISBN 978-1-7281-7378-8.
[IEEE 14th International Symposium on Applied Computational Intelligence and Informatics SACI 2020. Timisoara (RO), 21.05.2020-23.05.2020]
R&D Projects: GA MŠMT(CZ) 8A17006
Institutional support: RVO:67985556
Keywords : mixed data * Poisson distribution * mixture based clustering * passenger demand
OECD category: Statistics and probability
http://library.utia.cas.cz/separaty/2020/AS/uglickich-0524975.pdf
The paper deals with the task of modeling mixed continuous Gaussian and discrete Poisson data observed on a multimodal system. The proposed solution is based on recursive algorithms of Bayesian mixture estimation. The main contributions of the approach are: (i) the use of the discretized information of normal variables in the form of their clusters in order to keep the one-pass recursive estimation methodology and (ii) the prediction of the multimodal Poisson variable. Experiments with simulated and real data are presented.
Permanent Link: http://hdl.handle.net/11104/0309417
Number of the records: 1