bibtype C - Conference Paper (international conference)
ARLID 0447685
utime 20240103210642.3
mtime 20150925235959.9
SCOPUS 84943570009
WOS 000493121100085
title (primary) (eng) How matroids occur in the context of learning Bayesian network structure
specification
page_count 10 s.
media_type P
serial
ARLID cav_un_epca*0447684
ISBN 978-0-9966431-0-8
title Uncertainty in Artificial Intelligence, Proceedings of the Thirty-First Conference (2015)
page_num 832-841
publisher
place Corvallis, Oregon
name AUAI Press
year 2015
keyword learning Bayesian network structure
keyword matroid
keyword family-variable polytope
author (primary)
ARLID cav_un_auth*0101202
full_dept Department of Decision Making Theory
name1 Studený
name2 Milan
institution UTIA-B
full_dept (cz) Matematická teorie rozhodování
full_dept (eng) Department of Decision Making Theory
department (cz) MTR
department (eng) MTR
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url http://library.utia.cas.cz/separaty/2015/MTR/studeny-0447685.pdf
cas_special
project
ARLID cav_un_auth*0292670
project_id GA13-20012S
agency GA ČR
abstract (eng) It is shown that any connected matroid having a non-trivial cluster of BN variables as its ground set induces a facet-defining inequality for the polytope(s) used in the ILP approach to globally optimal BN structure learning. The result applies to well-known k-cluster inequalities, which play a crucial role in the ILP approach.
action
ARLID cav_un_auth*0319865
name 31st Conference on Uncertainty in Artificial Intelligence
dates 12.07.2015-16.07.2015
place Amsterdam
country NL
RIV BA
reportyear 2016
num_of_auth 1
presentation_type PR
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0249568
confidential S
arlyear 2015
mrcbU14 84943570009 SCOPUS
mrcbU34 000493121100085 WOS
mrcbU63 cav_un_epca*0447684 Uncertainty in Artificial Intelligence, Proceedings of the Thirty-First Conference (2015) 978-0-9966431-0-8 832 841 Corvallis, Oregon AUAI Press 2015