| bibtype |
J -
Journal Article
|
| ARLID |
0425539 |
| utime |
20240103203939.7 |
| mtime |
20140226235959.9 |
| WOS |
000342540700007 |
| SCOPUS |
84894058260 |
| DOI |
10.1016/j.ins.2014.01.048 |
| title
(primary) (eng) |
Approximate Bayesian recursive estimation |
| specification |
| page_count |
12 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0256752 |
| ISSN |
0020-0255 |
| title
|
Information Sciences |
| volume_id |
285 |
| volume |
1 (2014) |
| page_num |
100-111 |
| publisher |
|
|
| keyword |
Approximate parameter estimation |
| keyword |
Bayesian recursive estimation |
| keyword |
Kullback–Leibler divergence |
| keyword |
Forgetting |
| author
(primary) |
| ARLID |
cav_un_auth*0101124 |
| name1 |
Kárný |
| name2 |
Miroslav |
| 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 |
| share |
100 |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
GA13-13502S |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0292725 |
|
| abstract
(eng) |
Bayesian learning provides a firm theoretical basis of the design and exploitation of algorithms in data-streams processing (preprocessing, change detection, hypothesis testing, clustering, etc.). Primarily, it relies on a recursive parameter estimation of a firmly bounded complexity. As a rule, it has to approximate the exact posterior probability density (pd), which comprises unreduced information about the estimated parameter. In the recursive treatment of the data stream, the latest approximate pd is usually updated using the treated parametric model and the newest data and then approximated. The fact that approximation errors may accumulate over time course is mostly neglected in the estimator design and, at most, checked ex post. The paper inspects the estimator design with respect to the error accumulation and concludes that a sort of forgetting (pd flattening) is an indispensable part of a reliable approximate recursive estimation. |
| reportyear |
2015 |
| RIV |
BB |
| num_of_auth |
1 |
| mrcbC52 |
4 A 4a 20231122140119.7 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0231504 |
| confidential |
S |
| mrcbT16-e |
COMPUTERSCIENCEINFORMATIONSYSTEMS |
| mrcbT16-j |
0.873 |
| mrcbT16-s |
2.226 |
| mrcbT16-4 |
Q1 |
| mrcbT16-B |
74.327 |
| mrcbT16-C |
96.043 |
| mrcbT16-D |
Q2 |
| mrcbT16-E |
Q1* |
| arlyear |
2014 |
| mrcbTft |
\nSoubory v repozitáři: karny-0425539.pdf |
| mrcbU14 |
84894058260 SCOPUS |
| mrcbU34 |
000342540700007 WOS |
| mrcbU63 |
cav_un_epca*0256752 Information Sciences 0020-0255 1872-6291 Roč. 285 č. 1 2014 100 111 Elsevier |
|