TY - GEN
T1 - Provisional propagation for verifying monotonicity of Bayesian networks
AU - Rietbergen, M.T.
AU - van der Gaag, L.C.
AU - Bodlaender, H.L.
PY - 2014
Y1 - 2014
N2 - Many real-world Bayesian networks are expected to exhibit commonly known properties of monotonicity. Since monotonicity violations may be introduced despite careful engineering efforts, these properties need be verified before using a network in practice. We will show that the problem of verifying monotonicity in general has a prohibitively high computational complexity. We will argue however, that the runtime complexity involved can be substantially reduced by using a tailored algorithm which we coined provisional propagation. By means of this algorithm in fact, verifying monotonicity may become feasible for a range of real-world networks.
AB - Many real-world Bayesian networks are expected to exhibit commonly known properties of monotonicity. Since monotonicity violations may be introduced despite careful engineering efforts, these properties need be verified before using a network in practice. We will show that the problem of verifying monotonicity in general has a prohibitively high computational complexity. We will argue however, that the runtime complexity involved can be substantially reduced by using a tailored algorithm which we coined provisional propagation. By means of this algorithm in fact, verifying monotonicity may become feasible for a range of real-world networks.
UR - https://www.scopus.com/pages/publications/84923163697
U2 - 10.3233/978-1-61499-419-0-759
DO - 10.3233/978-1-61499-419-0-759
M3 - Conference contribution
AN - SCOPUS:84923163697
T3 - Frontiers in Artificial Intelligence and Applications
SP - 759
EP - 764
BT - ECAI 2014 - 21st European Conference on Artificial Intelligence, Including Prestigious Applications of Intelligent Systems, PAIS 2014, Proceedings
PB - IOS Press
T2 - 21st European Conference on Artificial Intelligence, ECAI 2014
Y2 - 18 August 2014 through 22 August 2014
ER -