Denis D. Mauá's
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    • Modeling Markov Decision Processes with Imprecise Probabilities Using Probabilistic Logic Programming
    • On the complexity of propositional and relational credal networks
    • On the Semantics and Complexity of Probabilistic Logic Programs
    • On Using Sum-Product Networks For Multi-Label Classification
    • Parameter Learning in ProbLog with Probabilistic Rules
    • Speeding-up ProbLog's Parameter Learning
    • The Complexity of Inferences and Explanations in Probabilistic Logic Programming
    • The Descriptive Complexity of Bayesian Network Specifications
    • The effect of combination functions on the complexity of relational Bayesian networks
    • University Entrance Exam as a Guiding Test for Artificial Intelligence
    • The Complexity of Bayesian Networks Specified by Propositional and Relational Languages
    • Better Initialization Heuristics for Order-based Bayesian Network Structure Learning
    • Equivalences between maximum a posteriori inference in Bayesian networks and maximum expected utility computation in influence diagrams
    • Fast local search methods for solving limited memory influence diagrams
    • Hidden Markov models with set-valued parameters
    • Improving Acyclic Selection Order-Based Bayesian Network Structure Learning
    • Markov Decision Processes Specified by Probabilistic Logic Programming: Representation and Solution
    • Probabilistic Graphical Models Specified by Probabilistic Logic Programs: Semantics and Complexity
    • The Effect of Combination Functions on the Complexity of Relational Bayesian Networks
    • The structure and complexity of credal semantics
    • The Well-Founded Semantics of Cyclic Probabilistic Logic Programs
    • A Tractable Class of Model Counting Problems
    • Bayesian Networks of Bounded Treewith: A Performance Analysis
    • Bayesian Networks Specified Using Propositional and Relational Constructs: Combined, Data, and Domain Complexity
    • DL-Lite Bayesian Networks: A Tractable Probabilistic Graphical Model
    • Early classification of time series by hidden Markov models with set-valued parameters
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    • On the Complexity of Propositional and Relational Credal Networks
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    • Equivalences between Maximum a Posteriori Inference in Bayesian Networks and Maximum Expected Utility Computation in Influence Diagrams
    • Probabilistic Inference in Credal Networks: New Complexity Results
    • Speeding Up k-Neighborhood Local Search in Limited Memory Influence Diagrams
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    • An Ensemble of Bayesian Networks for Multilabel Classification
    • On the complexity of solving polytree-shaped limited memory influence diagrams with binary variables
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    • Anytime Marginal MAP Inference
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Fast local search methods for solving limited memory influence diagrams

Jan 1, 2016·
Denis Deratani Mauá
,
Fabio Gagliardi Cozman
· 0 min read
PDF Cite DOI
Type
2
Publication
International Journal of Approximate Reasoning
Last updated on Jan 1, 2016
← Equivalences between maximum a posteriori inference in Bayesian networks and maximum expected utility computation in influence diagrams Jan 1, 2016
Hidden Markov models with set-valued parameters Jan 1, 2016 →

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