| bibtype |
J -
Journal Article
|
| ARLID |
0473911 |
| utime |
20240103213953.2 |
| mtime |
20170412235959.9 |
| SCOPUS |
85015609196 |
| WOS |
000400231600008 |
| DOI |
10.1016/j.ijar.2017.02.001 |
| title
(primary) (eng) |
Optimal design of priors constrained by external predictors |
| specification |
| page_count |
9 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0256774 |
| ISSN |
0888-613X |
| title
|
International Journal of Approximate Reasoning |
| volume_id |
84 |
| volume |
1 (2017) |
| page_num |
150-158 |
| publisher |
|
|
| keyword |
Fully probabilistic design |
| keyword |
Parameter prior |
| keyword |
External predictive distribution |
| keyword |
Bayesian transfer learning |
| keyword |
Kullback–Leibler divergence |
| author
(primary) |
| ARLID |
cav_un_auth*0021112 |
| name1 |
Quinn |
| name2 |
A. |
| country |
IE |
|
| 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 |
| name1 |
Kárný |
| name2 |
Miroslav |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101092 |
| name1 |
Guy |
| name2 |
Tatiana Valentine |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
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 |
| ARLID |
cav_un_auth*0331019 |
| project_id |
GA16-09848S |
| agency |
GA ČR |
| country |
CZ |
|
| abstract
(eng) |
This paper exploits knowledge made available by an external source in the form of a predictive distribution in order to elicit a parameter prior. It uses the terminology of Bayesian transfer learning, one of many domains dealing with reasoning as coherent knowledge processing. An empirical solution of the addressed problem was provided in [19], based on an interpretation of the external predictor as an empirical distribution constructed from fictitious data. In this paper, two main contributions are provided. First, the problem is solved using formal hierarchical Bayesian modeling [25], and the knowledge transfer is achieved optimally, i.e. in the minimum-KLD sense. Second, this hierarchical setting yields a distribution on the set of possible priors, with the choice [19] acting as the base distribution. This allows randomized choices of the prior to be generated, avoiding costly and/or intractable estimation of this prior. It also provides measures of uncertainty in the prior choice, allowing subsequent learning tasks to be assessed for robustness to this prior choice. The instantiation of the method in already published applications in knowledge elicitation, recursive learning and flat cooperation of adaptive controllers is recalled, and prospective application domains are also mentioned. |
| RIV |
BC |
| FORD0 |
10000 |
| FORD1 |
10200 |
| FORD2 |
10201 |
| reportyear |
2018 |
| num_of_auth |
3 |
| mrcbC52 |
4 A hod 4ah 20231122142404.6 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0271347 |
| cooperation |
| ARLID |
cav_un_auth*0345684 |
| name |
Trinity College Dublin, the University of Dublin |
| institution |
TCD |
| country |
IE |
|
| mrcbC64 |
1 Department of Adaptive Systems UTIA-B 10201 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE |
| confidential |
S |
| mrcbC86 |
2 Article|Proceedings Paper Computer Science Artificial Intelligence |
| mrcbC86 |
2 Article|Proceedings Paper Computer Science Artificial Intelligence |
| mrcbC86 |
2 Article|Proceedings Paper Computer Science Artificial Intelligence |
| mrcbT16-e |
COMPUTERSCIENCEARTIFICIALINTELLIGENCE |
| mrcbT16-j |
0.658 |
| mrcbT16-s |
0.866 |
| mrcbT16-B |
44.33 |
| mrcbT16-D |
Q3 |
| mrcbT16-E |
Q2 |
| arlyear |
2017 |
| mrcbTft |
\nSoubory v repozitáři: guy-0473911.pdf |
| mrcbU14 |
85015609196 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000400231600008 WOS |
| mrcbU63 |
cav_un_epca*0256774 International Journal of Approximate Reasoning 0888-613X 1873-4731 Roč. 84 č. 1 2017 150 158 Elsevier |
|