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Value‐based potentials: Exploiting quantitative information regularity patterns in probabilistic graphical models

International journal of intelligent systems, 2021-11, Vol.36 (11), p.6913-6943

2021

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Computation of Kullback–Leibler Divergence in Bayesian Networks

Entropy (Basel, Switzerland), 2021-08, Vol.23 (9), p.1122

2021

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Approximate Counting of Graphical Models via MCMC Revisited

International journal of intelligent systems, 2015-03, Vol.30 (3), p.384-420

2015

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Imprecise probabilistic models based on hierarchical intervals

Information sciences, 2023-08, Vol.638, p.118995, Article 118995

2023

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Using Value-Based Potentials for Making Approximate Inference on Probabilistic Graphical Models

Mathematics (Basel), 2022-07, Vol.10 (14), p.2542

2022

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A Bayesian approach to abrupt concept drift

Knowledge-based systems, 2019-12, Vol.185, p.104909, Article 104909

2019

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An Extended Approach to Learning Recursive Probability Trees from Data

International journal of intelligent systems, 2015-03, Vol.30 (3), p.355-383

2015

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Evaluating interval-valued influence diagrams

International journal of approximate reasoning, 2017-01, Vol.80, p.393-411

2017

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MPE Computation in Bayesian Networks Using Mini-Bucket and Probability Trees Approximation

International journal of uncertainty, fuzziness, and knowledge-based systems, 2020-10, Vol.28 (5), p.785-805

2020

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Learning marginal AMP chain graphs under faithfulness revisited

International journal of approximate reasoning, 2016-01, Vol.68, p.108-126

2016

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Variable Elimination for Interval-Valued Influence Diagrams

Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2015, p.541-551

2015

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Using Binary Trees for the Evaluation of Influence Diagrams

International journal of uncertainty, fuzziness, and knowledge-based systems, 2016-02, Vol.24 (1), p.59-89

2016

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Improvements to Variable Elimination and Symbolic Probabilistic Inference for evaluating Influence Diagrams

International journal of approximate reasoning, 2016-03, Vol.70, p.13-35

2016

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Locally averaged Bayesian Dirichlet metrics for learning the structure and the parameters of Bayesian networks

International journal of approximate reasoning, 2013-06, Vol.54 (4), p.526-540

2013

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Learning recursive probability trees from probabilistic potentials

International journal of approximate reasoning, 2012-12, Vol.53 (9), p.1367-1387

2012

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On SPI-Lazy Evaluation of Influence Diagrams

Probabilistic Graphical Models, 2014, Vol.8754, p.97-112

2014

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Extended Probability Trees for Probabilistic Graphical Models

Probabilistic Graphical Models, 2014, Vol.8754, p.113-128

2014

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FAST FACTORISATION OF PROBABILISTIC POTENTIALS AND ITS APPLICATION TO APPROXIMATE INFERENCE IN BAYESIAN NETWORKS

International journal of uncertainty, fuzziness, and knowledge-based systems, 2012-04, Vol.20 (2), p.223-243

2012

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On SPI for Evaluating Influence Diagrams

Information Processing and Management of Uncertainty in Knowledge-Based Systems, 2014, p.506-516

2014

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Estimating Conditional Probabilities by Mixtures of Low Order Conditional Distributions

Scalable Uncertainty Management, 2017, Vol.10564, p.107-118

2017

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Virtual Subconcept Drift Detection in Discrete Data Using Probabilistic Graphical Models

Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications, 2018, Vol.855, p.616-628

2018

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Approximate Lazy Evaluation of Influence Diagrams

Advances in Artificial Intelligence, 2013, p.321-331

2013

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Inference in Bayesian Networks with Recursive Probability Trees: Data Structure Definition and Operations

International journal of intelligent systems, 2013-07, Vol.28 (7), p.623-647

2013

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Learning Recursive Probability Trees from Data

Advances in Artificial Intelligence, 2013, p.332-341

2013

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Binary Probability Trees for Bayesian Networks Inference

Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2009, p.180-191

2009

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Value‐based potentials: Exploiting quantitative information regularity patterns in probabilistic graphical models

Computation of Kullback–Leibler Divergence in Bayesian Networks

Approximate Counting of Graphical Models via MCMC Revisited

Imprecise probabilistic models based on hierarchical intervals

Using Value-Based Potentials for Making Approximate Inference on Probabilistic Graphical Models

A Bayesian approach to abrupt concept drift

An Extended Approach to Learning Recursive Probability Trees from Data

Evaluating interval-valued influence diagrams

MPE Computation in Bayesian Networks Using Mini-Bucket and Probability Trees Approximation

Learning marginal AMP chain graphs under faithfulness revisited

Variable Elimination for Interval-Valued Influence Diagrams

Using Binary Trees for the Evaluation of Influence Diagrams

Improvements to Variable Elimination and Symbolic Probabilistic Inference for evaluating Influence Diagrams

Locally averaged Bayesian Dirichlet metrics for learning the structure and the parameters of Bayesian networks

Learning recursive probability trees from probabilistic potentials

On SPI-Lazy Evaluation of Influence Diagrams

Extended Probability Trees for Probabilistic Graphical Models

FAST FACTORISATION OF PROBABILISTIC POTENTIALS AND ITS APPLICATION TO APPROXIMATE INFERENCE IN BAYESIAN NETWORKS

On SPI for Evaluating Influence Diagrams

Estimating Conditional Probabilities by Mixtures of Low Order Conditional Distributions

Virtual Subconcept Drift Detection in Discrete Data Using Probabilistic Graphical Models

Approximate Lazy Evaluation of Influence Diagrams

Inference in Bayesian Networks with Recursive Probability Trees: Data Structure Definition and Operations

Learning Recursive Probability Trees from Data

Binary Probability Trees for Bayesian Networks Inference

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