bibtype C - Conference Paper (international conference)
ARLID 0544576
utime 20240111141054.5
mtime 20210810235959.9
DOI 10.5220/0010575006000608
title (primary) (eng) Prediction of Multimodal Poisson Variable using Discretization of Gaussian Data
specification
page_count 9 s.
media_type E
serial
ARLID cav_un_epca*0543770
ISBN 978-989-758-522-7
ISSN 2184-2809
title Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics
page_num 600-608
publisher
place Setúbal
name Scitepress
year 2021
editor
name1 Gusikhin
name2 O.
editor
name1 Nijmeijer
name2 H.
editor
name1 Madani
name2 K.
keyword Poisson Distribution Prediction
keyword Discrete Data
keyword Discretization
keyword Mixture based Clustering
keyword Bayesian Recursive Mixture Estimation
author (primary)
ARLID cav_un_auth*0383037
name1 Uglickich
name2 Evženie
institution UTIA-B
full_dept (cz) Zpracování signálů
full_dept (eng) Department of Signal Processing
department (cz) ZS
department (eng) ZS
full_dept Department of Signal Processing
country RU
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0101167
name1 Nagy
name2 Ivan
institution UTIA-B
full_dept (cz) Zpracování signálů
full_dept Department of Signal Processing
department (cz) ZS
department ZS
full_dept Department of Signal Processing
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0349960
name1 Petrouš
name2 Matej
institution UTIA-B
full_dept (cz) Zpracování signálů
full_dept Department of Signal Processing
department (cz) ZS
department ZS
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
source_type pdf
url http://library.utia.cas.cz/separaty/2021/ZS/uglickich-0544576.pdf
cas_special
project
project_id 8A19009
agency GA MŠk
country CZ
ARLID cav_un_auth*0385121
abstract (eng) 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.
action
ARLID cav_un_auth*0411344
name International Conference on Informatics in Control, Automation and Robotics 2021 /18./
dates 20210706
mrcbC20-s 20210708
place Setúbal (online)
country PT
RIV BB
FORD0 10000
FORD1 10100
FORD2 10103
reportyear 2022
num_of_auth 3
presentation_type PR
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0321816
confidential S
arlyear 2021
mrcbU14 SCOPUS
mrcbU24 PUBMED
mrcbU34 WOS
mrcbU56 pdf
mrcbU63 cav_un_epca*0543770 Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics Scitepress 2021 Setúbal 600 608 978-989-758-522-7 2184-2809
mrcbU67 Gusikhin O. 340
mrcbU67 Nijmeijer H. 340
mrcbU67 Madani K. 340