Prediction of Multimodal Poisson Variable using Discretization of Gaussian Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F21%3A00544576" target="_blank" >RIV/67985556:_____/21:00544576 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21260/21:00350794

Výsledek na webu

<a href="http://dx.doi.org/10.5220/0010575006000608" target="_blank" >http://dx.doi.org/10.5220/0010575006000608</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0010575006000608" target="_blank" >10.5220/0010575006000608</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Prediction of Multimodal Poisson Variable using Discretization of Gaussian Data

Popis výsledku v původním jazyce

The paper deals with predicting a discrete target variable described by the Poisson distribution based on the discretized Gaussian explanatory data under condition of the multimodality of a system observed. The discretization is performed using the recursive mixture-based clustering algorithms under Bayesian methodology. The proposed approach allows to estimate the Gaussian and Poisson models existing for each discretization interval of explanatory data and use them for the prediction. The main contributions of the approach include: (i) modeling the Poisson variable based on the cluster analysis of explanatory continuous data, (ii) the discretization approach based on recursive mixture estimation theory, (iii) the online prediction of the Poisson variable based on available Gaussian data discretized in real time. Results of illustrative experiments and comparison with the Poisson regression is demonstrated.

Název v anglickém jazyce

Prediction of Multimodal Poisson Variable using Discretization of Gaussian Data

Popis výsledku anglicky

The paper deals with predicting a discrete target variable described by the Poisson distribution based on the discretized Gaussian explanatory data under condition of the multimodality of a system observed. The discretization is performed using the recursive mixture-based clustering algorithms under Bayesian methodology. The proposed approach allows to estimate the Gaussian and Poisson models existing for each discretization interval of explanatory data and use them for the prediction. The main contributions of the approach include: (i) modeling the Poisson variable based on the cluster analysis of explanatory continuous data, (ii) the discretization approach based on recursive mixture estimation theory, (iii) the online prediction of the Poisson variable based on available Gaussian data discretized in real time. Results of illustrative experiments and comparison with the Poisson regression is demonstrated.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/8A19009" target="_blank" >8A19009: Arrowhead Tools for Engineering of Digitalisation Solutions</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics

ISBN

978-989-758-522-7

ISSN

2184-2809

e-ISSN

—

Počet stran výsledku

9

Strana od-do

600-608

Název nakladatele

Scitepress

Místo vydání

Setúbal

Místo konání akce

Setúbal (online)

Datum konání akce

6. 7. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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