| bibtype |
J -
Journal Article
|
| ARLID |
0524681 |
| utime |
20240103224115.3 |
| mtime |
20200602235959.9 |
| WOS |
000540209500005 |
| SCOPUS |
85085638278 |
| DOI |
10.1016/j.ijar.2020.04.007 |
| title
(primary) (eng) |
Non-linear failure rate: A Bayes study using Hamiltonian Monte Carlo simulation |
| specification |
| page_count |
22 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0256774 |
| ISSN |
0888-613X |
| title
|
International Journal of Approximate Reasoning |
| volume_id |
123 |
| volume |
1 (2020) |
| page_num |
55-76 |
| publisher |
|
|
| keyword |
Non-linear failure rate |
| keyword |
Bayesian estimators |
| keyword |
Hamiltonian Monte Carlo |
| author
(primary) |
| ARLID |
cav_un_auth*0101227 |
| share |
20 |
| name1 |
Volf |
| name2 |
Petr |
| institution |
UTIA-B |
| full_dept (cz) |
Stochastická informatika |
| full_dept (eng) |
Department of Stochastic Informatics |
| department (cz) |
SI |
| department (eng) |
SI |
| full_dept |
Department of Stochastic Informatics |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0392672 |
| share |
40 |
| name1 |
Thach |
| name2 |
T. |
| country |
CZ |
|
| author
|
| ARLID |
cav_un_auth*0265056 |
| share |
20 |
| name1 |
Briš |
| name2 |
R. |
| country |
CZ |
|
| author
|
| ARLID |
cav_un_auth*0392673 |
| share |
20 |
| name1 |
Coolen |
| name2 |
F. |
| country |
GB |
|
| source |
|
| source |
|
| cas_special |
| abstract
(eng) |
A non-linear failure ratemodel is introduced, analyzed, and applied to real data sets for both censored and uncensored data. The Hamiltonian Monte Carlo and cross-entropy methods have been exploited to empower the traditional methods of statistical estimation. Bayes estimators of parameters and reliability characteristics uses the Hamiltonian Monte Carlo and these estimators are considered under both symmetric and asymmetric loss functions. Additionally, the maximum likelihood estimators of parameters are obtained by using the cross-entropy method to optimize the log-likelihood function. The superiority of the proposed model and estimation procedures are demonstrated on real data sets. |
| result_subspec |
WOS |
| RIV |
BB |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2021 |
| num_of_auth |
4 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0309168 |
| confidential |
S |
| mrcbC86 |
3+4 Article Computer Science Artificial Intelligence |
| mrcbC91 |
C |
| mrcbT16-e |
COMPUTERSCIENCEARTIFICIALINTELLIGENCE |
| mrcbT16-i |
1.02111 |
| mrcbT16-j |
0.727 |
| mrcbT16-s |
1.039 |
| mrcbT16-B |
40.175 |
| mrcbT16-D |
Q3 |
| mrcbT16-E |
Q1 |
| arlyear |
2020 |
| mrcbU14 |
85085638278 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000540209500005 WOS |
| mrcbU63 |
cav_un_epca*0256774 International Journal of Approximate Reasoning 0888-613X 1873-4731 Roč. 123 č. 1 2020 55 76 Elsevier |
|