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 -