| bibtype |
B -
Monography
|
| ARLID |
0477300 |
| utime |
20240111140944.5 |
| mtime |
20170828235959.9 |
| ISBN |
978-3-319-64670-1 |
| WOS |
000446970800007 |
| DOI |
10.1007/978-3-319-64671-8 |
| title
(primary) (eng) |
Algorithms and Programs of Dynamic Mixture Estimation. Unified Approach to Different Types of Components |
| publisher |
| place |
Cham |
| name |
Springer |
| pub_time |
2017 |
|
| specification |
| page_count |
113 s. |
| media_type |
P |
|
| edition |
| name |
SpringerBriefs in Statistics |
|
| keyword |
dynamic mixture |
| keyword |
recursive mixture estimation |
| keyword |
algorithms and programs |
| author
(primary) |
| ARLID |
cav_un_auth*0101167 |
| name1 |
Nagy |
| name2 |
Ivan |
| institution |
UTIA-B |
| full_dept (cz) |
Zpracování signálů |
| full_dept (eng) |
Department of Signal Processing |
| department (cz) |
ZS |
| department (eng) |
ZS |
| full_dept |
Department of Signal Processing |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0108105 |
| name1 |
Suzdaleva |
| name2 |
Evgenia |
| institution |
UTIA-B |
| full_dept (cz) |
Zpracování signálů |
| full_dept |
Department of Signal Processing |
| department (cz) |
ZS |
| department |
ZS |
| full_dept |
Department of Signal Processing |
| country |
RU |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0321440 |
| project_id |
GA15-03564S |
| agency |
GA ČR |
|
| abstract
(eng) |
This book provides a general theoretical background for constructing the recursive Bayesian estimation algorithms for mixture models. It collects the recursive algorithms for estimating dynamic mixtures of various distributions and brings them in the unified form, providing a scheme for constructing the estimation algorithm for a mixture of components modeled by distributions with reproducible statistics. It offers the recursive estimation of dynamic mixtures, which are free of iterative processes and close to analytical solutions as much as possible. In addition, these methods can be used online and simultaneously perform learning, which improves their efficiency during estimation. The book includes detailed program codes for solving the presented theoretical tasks. Codes are implemented in the open source platform for engineering computations. The program codes given serve to illustrate the theory and demonstrate the work of the included algorithms. |
| RIV |
BB |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2018 |
| num_of_auth |
2 |
| mrcbC52 |
4 A hod 4ah 20231122142612.3 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0274040 |
| mrcbC64 |
1 Department of Signal Processing UTIA-B 10103 STATISTICS & PROBABILITY |
| confidential |
S |
| mrcbC83 |
RIV/67985556:_____/17:00477300!RIV18-AV0-67985556 191975678 Doplnění UT WOS |
| mrcbC83 |
RIV/67985556:_____/17:00477300!RIV18-GA0-67985556 191965022 Doplnění UT WOS |
| mrcbC86 |
3+4 Article Statistics Probability |
| mrcbC86 |
3+4 Article Statistics Probability |
| mrcbC86 |
3+4 Article Statistics Probability |
| arlyear |
2017 |
| mrcbTft |
\nSoubory v repozitáři: nagy-0477300.pdf |
| mrcbU10 |
2017 |
| mrcbU10 |
Cham Springer |
| mrcbU12 |
978-3-319-64670-1 |
| mrcbU14 |
SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000446970800007 WOS |
| mrcbU56 |
pdf |
|