Modeling of mixed data for Poisson prediction

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F20%3A00524975" target="_blank" >RIV/67985556:_____/20:00524975 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/SACI49304.2020.9118836" target="_blank" >http://dx.doi.org/10.1109/SACI49304.2020.9118836</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/SACI49304.2020.9118836" target="_blank" >10.1109/SACI49304.2020.9118836</a>

Result language
angličtina
Original language name
Modeling of mixed data for Poisson prediction
Original language description
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.
Czech name
—
Czech description
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Project
<a href="/en/project/8A17006" target="_blank" >8A17006: (Ultra)Sound Interfaces and Low Energy iNtegrated SEnsors</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Applied Computational Intelligence and Informatics (SACI) : 2020 IEEE 14th International Symposium on Applied Computational Intelligence and Informatics (SACI)
ISBN
978-1-7281-7378-8
ISSN
—
e-ISSN
—
Number of pages
6
Pages from-to
77-82
Publisher name
IEEE
Place of publication
Piscataway
Event location
Timisoara
Event date
May 21, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000610510000012

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