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20122020
most citedThe Complexity of Approximately Solving Influence Diagrams

14 citations · 28 across the 5 of their papers we have counts for

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cs.AI20202 cited

Tractable Inference in Credal Sentential Decision Diagrams

Lilith Mattei, Alessandro Antonucci, Denis Deratani Mauá +2

Probabilistic sentential decision diagrams are logic circuits where the inputs of disjunctive gates are annotated by probability values. They allow for a compact representation of…

cs.AI20172 cited

Speeding-up ProbLog's Parameter Learning

Francisco H. O. V. de Faria, Arthur C. Gusmão, Fabio G. Cozman +1

ProbLog is a state-of-art combination of logic programming and probabilities; in particular ProbLog offers parameter learning through a variant of the EM algorithm. However, the re…

cs.AI2017

On the Semantics and Complexity of Probabilistic Logic Programs

Fabio Gagliardi Cozman, Denis Deratani Mauá

We examine the meaning and the complexity of probabilistic logic programs that consist of a set of rules and a set of independent probabilistic facts (that is, programs based on Sa…

cs.AI201214 cited

The Complexity of Approximately Solving Influence Diagrams

Denis D. Maua, Cassio Polpo de Campos, Marco Zaffalon

Influence diagrams allow for intuitive and yet precise description of complex situations involving decision making under uncertainty. Unfortunately, most of the problems described…

cs.AI201210 cited

Anytime Marginal MAP Inference

Denis Maua, Cassio De Campos

This paper presents a new anytime algorithm for the marginal MAP problem in graphical models. The algorithm is described in detail, its complexity and convergence rate are studied,…