| bibtype |
A -
Abstract
|
| ARLID |
0588431 |
| utime |
20240909091325.5 |
| mtime |
20240813235959.9 |
| title
(primary) (eng) |
Bayesian methods in neural networks for inverse atmospheric modelling |
| specification |
|
| serial |
| ARLID |
cav_un_epca*0597976 |
| title
|
Stochastic and Physical Monitoring Systems 2024 |
| publisher |
| place |
Praha |
| name |
CVUT |
| year |
2024 |
|
|
| keyword |
Bayesian methods |
| keyword |
mathematical modelling |
| keyword |
neural networks |
| author
(primary) |
| ARLID |
cav_un_auth*0464277 |
| name1 |
Brožová |
| name2 |
Antonie |
| institution |
UTIA-B |
| full_dept (cz) |
Adaptivní systémy |
| full_dept (eng) |
Department of Adaptive Systems |
| department (cz) |
AS |
| department (eng) |
AS |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101207 |
| name1 |
Šmídl |
| name2 |
Václav |
| institution |
UTIA-B |
| department |
AS |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0267768 |
| name1 |
Tichý |
| name2 |
Ondřej |
| institution |
UTIA-B |
| department |
AS |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| source |
|
| cas_special |
| project |
| project_id |
GA24-10400S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0464279 |
|
| abstract
(eng) |
Recovering a source and an amount of an emitted substance from distant measurement is an ill-posed problem. In this contribution, two methods based on Bayes theorem will be compared on a realistic toy problem with microplastics. First of them is a Bayesian neural network pretrained to mimic a lognormal process and second one is hierarchical variational model, where the parameters of the posterior distribution are modeled by a convolutional neural network. Both these approaches allow to incorporate spatial dependency of the locations of the source and offer an estimate of uncertainty to assess the reliability of the method.\n |
| action |
| ARLID |
cav_un_auth*0471752 |
| name |
Stochastic and Physical Monitoring Systems 2024 (SPMS 2024) /15./ |
| mrcbC20-d |
15. |
| dates |
20240620 |
| mrcbC20-s |
20240624 |
| place |
Dobřichovice |
| country |
CZ |
|
| RIV |
BC |
| FORD0 |
10000 |
| FORD1 |
10200 |
| FORD2 |
10201 |
| reportyear |
2025 |
| num_of_auth |
3 |
| presentation_type |
PO |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0355754 |
| mrcbC61 |
1 |
| confidential |
S |
| arlyear |
2024 |
| mrcbU14 |
SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
WOS |
| mrcbU63 |
cav_un_epca*0597976 Stochastic and Physical Monitoring Systems 2024 CVUT 2024 Praha |
|