Počet záznamů: 1  

Modeling of mixed data for Poisson prediction

  1. 1.
    0524975 - ÚTIA 2021 RIV US eng C - Konferenční příspěvek (zahraniční konf.)
    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]
    Grant CEP: GA MŠMT(CZ) 8A17006
    Institucionální podpora: RVO:67985556
    Klíčová slova: mixed data * Poisson distribution * mixture based clustering * passenger demand
    Obor OECD: 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.
    Trvalý link: http://hdl.handle.net/11104/0309417

     
     
Počet záznamů: 1  

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