| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0531345 |
| utime |
20240111141039.6 |
| mtime |
20200803235959.9 |
| SCOPUS |
85092640395 |
| DOI |
10.1109/INES49302.2020.9147173 |
| title
(primary) (eng) |
Rayleigh model fitting to nonnegative discrete data |
| specification |
| page_count |
6 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0531344 |
| ISBN |
978-1-7281-1059-2 |
| ISSN |
1543-9259 |
| title
|
Proceedings of 2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES) |
| page_num |
67-72 |
| publisher |
| place |
Piscataway |
| name |
IEEE |
| year |
2020 |
|
|
| keyword |
Poisson distribution |
| keyword |
multimodal data |
| keyword |
Rayleigh distribution |
| keyword |
recursive estimation |
| 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 modeling ordinal discrete random variables with a high number of nonnegative realizations. The prediction of the Rayleigh distribution learned on clusters of the explanatory variables is proposed. The proposed solution consists of the clustering and estimation phases based on the knowledge both of the target and explanatory variables, and the prediction phase using only the information from the explanatory variables. The main contributions of the approach are: (i) using the discretized knowledge of clusters of the explanatory variables and (ii) describing nonnegative discrete data by the multimodal Rayleigh distribution. Experiments with a data set from a tram network are provided. |
| action |
| ARLID |
cav_un_auth*0394315 |
| name |
IEEE International Conference on Intelligent Engineering Systems 2020 (INES 2020) /24./ |
| dates |
20200708 |
| mrcbC20-s |
20200710 |
| place |
Reykjavík |
| country |
IS |
|
| 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/11104/0310088 |
| confidential |
S |
| arlyear |
2020 |
| mrcbU14 |
85092640395 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
WOS |
| mrcbU56 |
pdf |
| mrcbU63 |
cav_un_epca*0531344 Proceedings of 2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES) 978-1-7281-1059-2 1543-9259 67 72 Piscataway IEEE 2020 |
|