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 |
|
editor |
|
editor |
|
|
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 |
|
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 |
|