| bibtype |
A -
Abstract
|
| ARLID |
0448581 |
| utime |
20240103210837.2 |
| mtime |
20151022235959.9 |
| title
(primary) (eng) |
Hierarchical Prior for Source Term Determination and Its Variational Bayes Estimation |
| specification |
| page_count |
1 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0448580 |
| title
|
CTBT: Science and Technology 2015 |
| page_num |
142-142 |
| publisher |
| place |
Vienna |
| name |
CTBTO |
| year |
2015 |
|
|
| keyword |
Bayesian analysis |
| keyword |
nonsupervised analysis |
| keyword |
parameterized priors |
| author
(primary) |
| ARLID |
cav_un_auth*0228606 |
| name1 |
Hofman |
| name2 |
Radek |
| 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. |
|
| author
|
| ARLID |
cav_un_auth*0101207 |
| name1 |
Šmídl |
| name2 |
Václav |
| 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. |
|
| author
|
| ARLID |
cav_un_auth*0267768 |
| name1 |
Tichý |
| name2 |
Ondřej |
| 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 |
| project_id |
7F14287 |
| agency |
GA MŠk |
| country |
CZ |
| ARLID |
cav_un_auth*0318110 |
|
| abstract
(eng) |
Tools for the fusion of atmospheric transport models with data are of a great importance in many fields where characteristics of a source term are sought. The most promising tools seem to be those based on Bayesian analysis, with the most appealing feature being the inherent capability to treat the full probability distribution of involved uncertainties. Since the output is a posterior probability distribution, probabilistic interpretation of results can be drawn. However, practical application of Bayesian methods can be difficult because a proper specification of the a prior distribution is needed. The tools must ensure that the prior selection procedure is robust enough to work under various circumstances, particularly in the case of continuously operating nonsupervised analysis. A solution to this problem can be the use of simple parameterized priors. Here, we present a method based on prior hyper-parametrization and estimation of these parameters using Variational Bayes method. |
| action |
| ARLID |
cav_un_auth*0320636 |
| name |
CTBT: Science and Technology 2015 |
| place |
Vienna |
| dates |
22.06.2015-26.06.2015 |
| country |
AT |
|
| reportyear |
2016 |
| RIV |
BB |
| num_of_auth |
3 |
| mrcbC52 |
4 O 4o 20231122141215.2 |
| presentation_type |
PO |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0250584 |
| confidential |
S |
| arlyear |
2015 |
| mrcbTft |
\nSoubory v repozitáři: 0448581.pdf |
| mrcbU63 |
cav_un_epca*0448580 CTBT: Science and Technology 2015 142 142 Vienna CTBTO 2015 |
|