TY - GEN

T1 - The descriptive complexity of Bayesian network specifications

AU - Cozman, Fabio G.

AU - Mauá, Denis D.

PY - 2017/1/1

Y1 - 2017/1/1

N2 - © Springer International Publishing AG 2017. We adapt the theory of descriptive complexity to Bayesian networks, by investigating how expressive can be specifications based on predicates and quantifiers. We show that Bayesian network specifications that employ first-order quantification capture the complexity class PP; that is, any phenomenon that can be simulated with a polynomial time probabilistic Turing machine can be also modeled by such a network. We also show that, by allowing quantification over predicates, the resulting Bayesian network specifications capture the complexity class PPNP, a result that does not seem to have equivalent in the literature.

AB - © Springer International Publishing AG 2017. We adapt the theory of descriptive complexity to Bayesian networks, by investigating how expressive can be specifications based on predicates and quantifiers. We show that Bayesian network specifications that employ first-order quantification capture the complexity class PP; that is, any phenomenon that can be simulated with a polynomial time probabilistic Turing machine can be also modeled by such a network. We also show that, by allowing quantification over predicates, the resulting Bayesian network specifications capture the complexity class PPNP, a result that does not seem to have equivalent in the literature.

UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85025146815&origin=inward

UR - https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85025146815&origin=inward

U2 - 10.1007/978-3-319-61581-3_9

DO - 10.1007/978-3-319-61581-3_9

M3 - Conference contribution

SN - 9783319615806

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 93

EP - 103

BT - The descriptive complexity of Bayesian network specifications

T2 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Y2 - 1 January 2018

ER -