| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0524975 |
| utime |
20240111141038.4 |
| mtime |
20200615235959.9 |
| SCOPUS |
85087422156 |
| WOS |
000610510000012 |
| DOI |
10.1109/SACI49304.2020.9118836 |
| title
(primary) (eng) |
Modeling of mixed data for Poisson prediction |
| specification |
| page_count |
6 s. |
| media_type |
E |
|
| serial |
| ARLID |
cav_un_epca*0525235 |
| ISBN |
978-1-7281-7378-8 |
| title
|
Applied Computational Intelligence and Informatics (SACI) : 2020 IEEE 14th International Symposium on Applied Computational Intelligence and Informatics (SACI) |
| page_num |
77-82 |
| publisher |
| place |
Piscataway |
| name |
IEEE |
| year |
2020 |
|
|
| keyword |
mixed data |
| keyword |
Poisson distribution |
| keyword |
mixture based clustering |
| keyword |
passenger demand |
| author
(primary) |
| ARLID |
cav_un_auth*0349960 |
| name1 |
Petrouš |
| name2 |
Matej |
| institution |
UTIA-B |
| full_dept (cz) |
Zpracování signálů |
| full_dept (eng) |
Department of Signal Processing |
| department (cz) |
ZS |
| department (eng) |
ZS |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0383037 |
| name1 |
Uglickich |
| name2 |
Evženie |
| 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 |
| country |
RU |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
8A17006 |
| agency |
GA MŠk |
| country |
CZ |
| ARLID |
cav_un_auth*0351997 |
|
| abstract
(eng) |
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. |
| action |
| ARLID |
cav_un_auth*0393003 |
| name |
IEEE 14th International Symposium on Applied Computational Intelligence and Informatics SACI 2020 |
| dates |
20200521 |
| mrcbC20-s |
20200523 |
| place |
Timisoara |
| country |
RO |
|
| RIV |
BB |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2021 |
| num_of_auth |
2 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0309417 |
| confidential |
S |
| mrcbC86 |
n.a. Proceedings Paper Computer Science Artificial Intelligence|Computer Science Information Systems|Computer Science Theory Methods |
| arlyear |
2020 |
| mrcbU14 |
85087422156 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000610510000012 WOS |
| mrcbU56 |
pdf |
| mrcbU63 |
cav_un_epca*0525235 Applied Computational Intelligence and Informatics (SACI) : 2020 IEEE 14th International Symposium on Applied Computational Intelligence and Informatics (SACI) 978-1-7281-7378-8 77 82 Piscataway IEEE 2020 |
|