bibtype |
C -
Conference Paper (international conference)
|
ARLID |
0410574 |
utime |
20240103182223.5 |
mtime |
20060210235959.9 |
ISBN |
1-55860-709-9 |
title
(primary) (eng) |
Marginalization in composed probabilistic models |
publisher |
place |
San Francisco |
name |
Morgan Kaufmann |
pub_time |
2000 |
|
specification |
|
serial |
title
|
Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence |
page_num |
36-43 |
editor |
|
editor |
name1 |
Goldszmidt |
name2 |
M. |
|
|
keyword |
multidimensional distribution |
keyword |
Bayesian network |
keyword |
computation |
author
(primary) |
ARLID |
cav_un_auth*0101118 |
name1 |
Jiroušek |
name2 |
Radim |
institution |
UTIA-B |
full_dept |
Department of Decision Making Theory |
fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
COSATI |
120 |
cas_special |
project |
project_id |
GA201/98/1487 |
agency |
GA ČR |
ARLID |
cav_un_auth*0005923 |
|
project |
project_id |
VS96008 |
agency |
GA MŠk |
ARLID |
cav_un_auth*0025049 |
|
research |
AV0Z1075907 |
abstract
(eng) |
Composition of low-dimensional distributions, whose foundations were laid in the paper published in the Proceedings of UAI'97, appeared to be an alternative apparatus to describe multidimensional probabilistic models. In contrast to Graphical Markov Models, which define multidimensional distributions in a declarative way, this approach is rather procedural. |
action |
ARLID |
cav_un_auth*0212764 |
name |
Conference on Uncertainty in Artificial Intelligence /16./ |
place |
Stanford |
country |
US |
dates |
30.06.2000-03.07.2000 |
|
RIV |
BA |
department |
MTR |
permalink |
http://hdl.handle.net/11104/0130663 |
ID_orig |
UTIA-B 20010043 |
arlyear |
2000 |
mrcbU10 |
2000 |
mrcbU10 |
San Francisco Morgan Kaufmann |
mrcbU12 |
1-55860-709-9 |
mrcbU63 |
Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence 36 43 |
mrcbU67 |
Boutilier C. 340 |
mrcbU67 |
Goldszmidt M. 340 |
|