| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0474383 |
| utime |
20240103214031.9 |
| mtime |
20170505235959.9 |
| SCOPUS |
85020008545 |
| WOS |
000418403500009 |
| DOI |
10.1007/978-3-319-54084-9_9 |
| title
(primary) (eng) |
Linear Inverse Problem with Range Prior on Correlations and Its Variational Bayes Inference |
| specification |
| page_count |
11 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0474382 |
| ISBN |
978-3-319-54084-9 |
| ISSN |
2194-1009 |
| title
|
Bayesian Statistics in Action: BAYSM 2016 |
| page_num |
91-101 |
| publisher |
| place |
Cham |
| name |
Springer International Publishing |
| year |
2017 |
|
| editor |
|
| editor |
|
| editor |
| name1 |
Villalobos |
| name2 |
I. |
|
| editor |
|
|
| keyword |
Linear inverse problem |
| keyword |
Variational Bayes inference |
| keyword |
Convex optimization |
| keyword |
Uncertain correlations |
| keyword |
Gamma dose rate measurements |
| keyword |
Nuclide ratios |
| author
(primary) |
| ARLID |
cav_un_auth*0267768 |
| name1 |
Tichý |
| name2 |
Ondřej |
| 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. |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0318110 |
| project_id |
7F14287 |
| agency |
GA MŠk |
| country |
CZ |
|
| abstract
(eng) |
The choice of regularization for an ill-conditioned linear inverse problem has significant impact on the resulting estimates. We consider a linear inverse model with on the solution in the form of zero mean Gaussian prior and with covariance matrix represented in modified Cholesky form. Elements of the covariance are considered as hyper-parameters with truncated Gaussian prior. The truncation points are obtained from expert judgment as range on correlations of selected elements of the solution. This model is motivated by estimation of mixture of radionuclides from gamma dose rate measurements under the prior knowledge on range of their ratios. Since we aim at high dimensional problems, we use the Variational Bayes inference procedure to derive approximate inference of the model. The method is illustrated and compared on a simple example and on more realistic 6 hours long release of mixture of 3 radionuclides. |
| action |
| ARLID |
cav_un_auth*0346112 |
| name |
Bayesian Young Statisticians Meeting 2016 |
| dates |
20160619 |
| mrcbC20-s |
20160621 |
| place |
Florence |
| country |
IT |
|
| RIV |
BB |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2018 |
| num_of_auth |
2 |
| presentation_type |
PO |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0271455 |
| confidential |
S |
| mrcbC86 |
3+4 Proceedings Paper Statistics Probability |
| mrcbC86 |
3+4 Proceedings Paper Statistics Probability |
| mrcbC86 |
3+4 Proceedings Paper Statistics Probability |
| arlyear |
2017 |
| mrcbU14 |
85020008545 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000418403500009 WOS |
| mrcbU63 |
cav_un_epca*0474382 Bayesian Statistics in Action: BAYSM 2016 978-3-319-54084-9 2194-1009 2194-1017 91 101 Cham Springer International Publishing 2017 Springer Proceedings in Mathematics & Statistics 194 |
| mrcbU67 |
340 Argiento R. |
| mrcbU67 |
340 Lanzarone E. |
| mrcbU67 |
340 Villalobos I. |
| mrcbU67 |
340 Mattei A. |
|