| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0531044 |
| utime |
20240103224239.4 |
| mtime |
20200720235959.9 |
| SCOPUS |
85089232840 |
| DOI |
10.1007/978-981-15-4917-5_28 |
| title
(primary) (eng) |
Compositional Models: Iterative Structure Learning from Data |
| specification |
| page_count |
16 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0531043 |
| ISBN |
978-981-15-4916-8 |
| title
|
Sensor Networks and Signal Processing |
| part_num |
vol. 176 |
| part_title |
Smart Innovation, Systems and Technologies |
| page_num |
379-395 |
| publisher |
| place |
Singapore |
| name |
Springer |
| year |
2021 |
|
| editor |
| name1 |
Peng |
| name2 |
Sheng-Lung |
|
| editor |
| name1 |
Favorskaya |
| name2 |
Margarita N. |
|
| editor |
| name1 |
Chao |
| name2 |
Han-Chieh |
|
|
| keyword |
Compositional models |
| keyword |
Structure learning |
| keyword |
Decomposability |
| keyword |
Likelihood-ratio |
| keyword |
Test statistics |
| author
(primary) |
| ARLID |
cav_un_auth*0216188 |
| name1 |
Kratochvíl |
| name2 |
Václav |
| institution |
UTIA-B |
| full_dept (cz) |
Matematická teorie rozhodování |
| full_dept (eng) |
Department of Decision Making Theory |
| department (cz) |
MTR |
| department (eng) |
MTR |
| full_dept |
Department of Decision Making Theory |
| country |
CZ |
| share |
25 |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0393863 |
| name1 |
Bína |
| name2 |
Vladislav |
| institution |
UTIA-B |
| full_dept (cz) |
Matematická teorie rozhodování |
| full_dept |
Department of Decision Making Theory |
| department (cz) |
MTR |
| department |
MTR |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101118 |
| name1 |
Jiroušek |
| name2 |
Radim |
| institution |
UTIA-B |
| full_dept (cz) |
Matematická teorie rozhodování |
| full_dept |
Department of Decision Making Theory |
| department (cz) |
MTR |
| department |
MTR |
| full_dept |
Department of Decision Making Theory |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0368377 |
| name1 |
Lee |
| name2 |
T. R. |
| country |
TW |
|
| source |
|
| cas_special |
| project |
| project_id |
GA19-06569S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0380559 |
|
| project |
| project_id |
MOST-04-18 |
| agency |
Akademie věd - GA AV ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0393867 |
|
| abstract
(eng) |
Multidimensional probability distributions that are too large to be stored in computer memory can be represented by a compositional model - a sequence of low-dimensional probability distributions that when composed together try to faithfully estimate the original multidimensional distribution. The decomposition to the compositional model is not satisfactorily resolved. We offer an approach based on search traversal through the decomposable model class using likelihood-test statistics. The paper is a work sketch of the current research. |
| action |
| ARLID |
cav_un_auth*0393865 |
| name |
Sensor Networks and Signal Processing (SNSP 2019) /2./ |
| dates |
20191119 |
| place |
Hualien |
| country |
TW |
| mrcbC20-s |
20191122 |
|
| reportyear |
2022 |
| RIV |
IN |
| FORD0 |
10000 |
| FORD1 |
10200 |
| FORD2 |
10201 |
| num_of_auth |
4 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0310094 |
| confidential |
S |
| arlyear |
2021 |
| mrcbU14 |
85089232840 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
WOS |
| mrcbU63 |
cav_un_epca*0531043 Sensor Networks and Signal Processing Smart Innovation, Systems and Technologies vol. 176 Springer 2021 Singapore 379 395 978-981-15-4916-8 2190-3018 |
| mrcbU67 |
Peng Sheng-Lung 340 |
| mrcbU67 |
Favorskaya Margarita N. 340 |
| mrcbU67 |
Chao Han-Chieh 340 |
|