TY - GEN
T1 - Evidence propagation in credal networks: An exact algorithm based on separately specified sets of probability
AU - da Rocha, José Carlos F.
AU - Cozman, Fabio G.
PY - 2002/1/1
Y1 - 2002/1/1
N2 - © Springer-Verlag Berlin Heidelberg 2002. Probabilistic models and graph-based independence languages have often been combined in artificial intelligence research. The Bayesian network formalism is probably the best example of this type of association. In this article we focus on graphical structures that associate graphs with sets of probability measures — the result is referred to as a credal network. We describe credal networks and review an algorithm for evidential reasoning that we have recently developed. The algorithm substantially simplifies the computation of upper and lower probabilities by exploiting an independence assumption (strong independence) and a representation based on separately specified sets of probability measures. The algorithm is particularly efficient when applied to polytree structures. We then discuss a strategy for approximate reasoning in multi-connected networks, based on conditioning.
AB - © Springer-Verlag Berlin Heidelberg 2002. Probabilistic models and graph-based independence languages have often been combined in artificial intelligence research. The Bayesian network formalism is probably the best example of this type of association. In this article we focus on graphical structures that associate graphs with sets of probability measures — the result is referred to as a credal network. We describe credal networks and review an algorithm for evidential reasoning that we have recently developed. The algorithm substantially simplifies the computation of upper and lower probabilities by exploiting an independence assumption (strong independence) and a representation based on separately specified sets of probability measures. The algorithm is particularly efficient when applied to polytree structures. We then discuss a strategy for approximate reasoning in multi-connected networks, based on conditioning.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=70349778220&origin=inward
UR - https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=70349778220&origin=inward
U2 - 10.1007/3-540-36127-8_36
DO - 10.1007/3-540-36127-8_36
M3 - Conference contribution
SN - 3540001247
SN - 9783540001249
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 376
EP - 385
BT - Evidence propagation in credal networks: An exact algorithm based on separately specified sets of probability
T2 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Y2 - 1 January 2018
ER -