| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0583574 |
| utime |
20240402215301.5 |
| mtime |
20240305235959.9 |
| SCOPUS |
85174447437 |
| WOS |
001155257400031 |
| DOI |
10.1007/978-3-031-42508-0_31 |
| title
(primary) (eng) |
On the Bayesian Interpretation of Penalized Statistical Estimators |
| specification |
| page_count |
10 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0579679 |
| ISBN |
978-3-031-42507-3 |
| title
|
Artificial Intelligence and Soft Computing. 22nd International Conference, ICAISC 2023, Proceedings, Part 2 |
| page_num |
343-352 |
| publisher |
| place |
Cham |
| name |
Springer |
| year |
2023 |
|
| editor |
|
| editor |
|
| editor |
| name1 |
Korytkowski |
| name2 |
M. |
|
| editor |
|
| editor |
| name1 |
Tadeusiewicz |
| name2 |
R. |
|
| editor |
|
|
| keyword |
Bayesian estimation |
| keyword |
regularization |
| keyword |
penalization |
| keyword |
robustness |
| keyword |
regression |
| author
(primary) |
| ARLID |
cav_un_auth*0345793 |
| name1 |
Kalina |
| name2 |
Jan |
| 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*0353498 |
| name1 |
Peštová |
| name2 |
B. |
| country |
CZ |
|
| source |
|
| cas_special |
| project |
| project_id |
GA21-05325S |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0409039 |
|
| abstract
(eng) |
The aim of this work is to search for intuitive interpretations of penalized statistical estimators. Penalized estimates of the parameters of three models obtained by Bayesian reasoning are explained here to correspond to the intuition. First, the paper considers Bayesian estimates of the mean and covariance matrix for the multivariate normal distribution. Second, a connection of a robust regularized version of Mahalanobis distance with Bayesian estimation is discussed. Third, regularization networks, which represent a common nonparametric tool for regression modeling, are presented as Bayesian methods as well. On the whole, selected important multivariate and/or regression models are considered and novel interpretations are formulated. |
| action |
| ARLID |
cav_un_auth*0460088 |
| name |
ICAISC 2023: International Conference on Artificial Intelligence and Soft Computing /22./ |
| dates |
20230718 |
| mrcbC20-s |
20230722 |
| place |
Zakopane |
| country |
PL |
|
| RIV |
BA |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2024 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0351579 |
| confidential |
S |
| arlyear |
2023 |
| mrcbU14 |
85174447437 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
001155257400031 WOS |
| mrcbU63 |
cav_un_epca*0579679 Artificial Intelligence and Soft Computing. 22nd International Conference, ICAISC 2023, Proceedings, Part 2 Springer 2023 Cham 343 352 978-3-031-42507-3 Lecture Notes in Computer Science 14126 |
| mrcbU67 |
Rutkowski L. 340 |
| mrcbU67 |
Scherer R. 340 |
| mrcbU67 |
Korytkowski M. 340 |
| mrcbU67 |
Pedrycz W. 340 |
| mrcbU67 |
Tadeusiewicz R. 340 |
| mrcbU67 |
Zurada J. M. 340 |
|