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
page_count 8 s.
serial
title Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
page_num 36-43
editor
name1 Boutilier
name2 C.
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