| bibtype |
J -
Journal Article
|
| ARLID |
0463052 |
| utime |
20240103212639.4 |
| mtime |
20160926235959.9 |
| SCOPUS |
84978967308 |
| WOS |
000383292500035 |
| DOI |
10.1016/j.ins.2016.07.035 |
| title
(primary) (eng) |
Fully probabilistic design of hierarchical Bayesian models |
| specification |
| page_count |
16 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0256752 |
| ISSN |
0020-0255 |
| title
|
Information Sciences |
| volume_id |
369 |
| volume |
1 (2016) |
| page_num |
532-547 |
| publisher |
|
|
| keyword |
Fully probabilistic design |
| keyword |
Ideal distribution |
| keyword |
Minimum cross-entropy principle |
| keyword |
Bayesian conditioning |
| keyword |
Kullback-Leibler divergence |
| keyword |
Bayesian nonparametric modelling |
| author
(primary) |
| ARLID |
cav_un_auth*0213041 |
| share |
34 |
| name1 |
Quinn |
| name2 |
A. |
| country |
IR |
|
| author
|
| ARLID |
cav_un_auth*0101124 |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| full_dept |
Department of Adaptive Systems |
| share |
33 |
| name1 |
Kárný |
| name2 |
Miroslav |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101092 |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| full_dept |
Department of Adaptive Systems |
| share |
33 |
| name1 |
Guy |
| name2 |
Tatiana Valentine |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0292725 |
| project_id |
GA13-13502S |
| agency |
GA ČR |
|
| abstract
(eng) |
The minimum cross-entropy principle is an established technique for design of an un- known distribution, processing linear functional constraints on the distribution. More generally, fully probabilistic design (FPD) chooses the distribution-within the knowledge-constrained set of possible distributions-for which the Kullback-Leibler divergence to the designer’s ideal distribution is minimized. These principles treat the unknown distribution deterministically. In this paper, fully probabilistic design is applied to hierarchical Bayesian models for the first time, yielding optimal design of a (possibly nonparametric) stochastic model for the unknown distribution. This equips minimum cross-entropy and FPD distributional estimates with measures of uncertainty. It enables robust choice of the optimal model, as well as randomization of this choice. The ability to process non-linear functional constraints in the constructed distribution significantly extends the applicability of these principles. |
| RIV |
BB |
| reportyear |
2017 |
| num_of_auth |
3 |
| mrcbC52 |
4 A hod 4ah 20231122141902.5 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0262369 |
| cooperation |
| ARLID |
cav_un_auth*0333787 |
| name |
Trinity College Dublin |
| country |
IE |
|
| mrcbC64 |
1 Department of Adaptive Systems UTIA-B 10201 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE |
| confidential |
S |
| mrcbC86 |
2 Article Computer Science Information Systems |
| mrcbT16-e |
COMPUTERSCIENCEINFORMATIONSYSTEMS |
| mrcbT16-j |
1.09 |
| mrcbT16-s |
1.781 |
| mrcbT16-4 |
Q1 |
| mrcbT16-B |
80.37 |
| mrcbT16-D |
Q1 |
| mrcbT16-E |
Q1 |
| arlyear |
2016 |
| mrcbTft |
\nSoubory v repozitáři: karny-0463052.pdf |
| mrcbU14 |
84978967308 SCOPUS |
| mrcbU34 |
000383292500035 WOS |
| mrcbU63 |
cav_un_epca*0256752 Information Sciences 0020-0255 1872-6291 Roč. 369 č. 1 2016 532 547 Elsevier |
|