| bibtype |
V -
Research Report
|
| ARLID |
0358653 |
| utime |
20240103195059.6 |
| mtime |
20110404235959.9 |
| title
(primary) (eng) |
Notes on projection based modelling of beta-distributed weights of a two-component mixture |
| publisher |
| place |
Praha |
| name |
ÚTIA AV ČR, v.v.i |
| pub_time |
2011 |
|
| specification |
|
| edition |
| name |
Research Report |
| volume_id |
2297 |
|
| keyword |
beta mixtures |
| keyword |
projection |
| keyword |
Bayesian modelling |
| author
(primary) |
| ARLID |
cav_un_auth*0242543 |
| name1 |
Dedecius |
| name2 |
Kamil |
| 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 |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
GA102/08/0567 |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0239566 |
|
| research |
CEZ:AV0Z10750506 |
| abstract
(eng) |
This report contains brief notes on estimation of beta-distributed weight of a Gaussian mixture. The results are directly applied in paper Kárný, M.: On approximate Bayesian recursive estimation]. First, we develop a method to update the beta distribution of weights by new data (evidences) and show, that a projection is needed to preserve the low modelling complexity. Then, we show how forgetting may be applied to improve adaptivity. The results can be immediately applied to multicomponent mixtures. |
| reportyear |
2012 |
| RIV |
BB |
| mrcbC52 |
4 O 4o 20231122134518.4 |
| permalink |
http://hdl.handle.net/11104/0196617 |
| arlyear |
2011 |
| mrcbTft |
\nSoubory v repozitáři: 0358653.pdf |
| mrcbU10 |
2011 |
| mrcbU10 |
Praha ÚTIA AV ČR, v.v.i |
|