| bibtype |
D -
Thesis
|
| ARLID |
0452795 |
| utime |
20240103211449.8 |
| mtime |
20160215235959.9 |
| title
(primary) (eng) |
Cross-entropy based combination of discrete probability distributions for distributed decision making |
| publisher |
| place |
Praha |
| name |
MFF UK |
| pub_time |
2015 |
|
| specification |
| page_count |
80 s. |
| media_type |
P |
|
| keyword |
distributed decision making |
| keyword |
minimum cross-entropy principle |
| keyword |
Kullback-Leibler divergence |
| author
(primary) |
| ARLID |
cav_un_auth*0263972 |
| name1 |
Sečkárová |
| name2 |
Vladimíra |
| full_dept (cz) |
Adaptivní systémy |
| full_dept (eng) |
Department of Adaptive Systems |
| department (cz) |
AS |
| department (eng) |
AS |
| institution |
UTIA-B |
| full_dept |
Department of Adaptive Systems |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
GA13-13502S |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0292725 |
|
| abstract
(eng) |
In this work we propose a systematic way to combine discrete probability distributions based on decision making theory and theory of information, namely the cross-entropy (also known as the Kullback-Leibler (KL) divergence). The optimal combination is a probability mass function minimizing the conditional expected KL-divergence. |
| reportyear |
2016 |
| RIV |
BB |
| habilitation |
| dates |
14.09.2015 |
| degree |
Ph.D. |
| institution |
Ústav teorie informace a automatizace AV ČR |
| place |
Praha |
| year |
2015 |
|
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0257075 |
| confidential |
S |
| arlyear |
2015 |
| mrcbU10 |
2015 |
| mrcbU10 |
Praha MFF UK |
|