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An interactive approach for Bayesian network learning using domain/expert knowledge

International journal of approximate reasoning, 2013-10, Vol.54 (8), p.1168-1181

2013

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InferPy: Probabilistic modeling with Tensorflow made easy

Knowledge-based systems, 2019-03, Vol.168, p.25-27

2019

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Learning Styles Impact Students’ Perceptions on Active Learning Methodologies: A Case Study on the Use of Live Coding and Short Programming Exercises

Education sciences, 2024-03, Vol.14 (3), p.250

2024

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Probabilistic Models with Deep Neural Networks

Entropy (Basel, Switzerland), 2021-01, Vol.23 (1), p.117

2021

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An ensemble method using credal decision trees

European journal of operational research, 2010-08, Vol.205 (1), p.218-226

2010

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An Empirical Analysis of the Impact of Continuous Assessment on the Final Exam Mark

Mathematics (Basel), 2022-11, Vol.10 (21), p.3994

2022

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Analyzing concept drift: A case study in the financial sector

Intelligent data analysis, 2020-01, Vol.24 (3), p.665-688

2020

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From anecdote to evidence: the relationship between personality and need for cognition of developers

Empirical software engineering : an international journal, 2022, Vol.27 (3), p.71-71, Article 71

2022

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Bagging schemes on the presence of class noise in classification

Expert systems with applications, 2012-06, Vol.39 (8), p.6827-6837

2012

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InferPy: Probabilistic modeling with deep neural networks made easy

Neurocomputing (Amsterdam), 2020-11, Vol.415, p.408-410

2020

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MAP inference in dynamic hybrid Bayesian networks

Progress in artificial intelligence, 2017-06, Vol.6 (2), p.133-144

2017

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Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks

International journal of approximate reasoning, 2018-09, Vol.100, p.115-134

2018

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New skeleton-based approaches for Bayesian structure learning of Bayesian networks

Applied soft computing, 2013-02, Vol.13 (2), p.1110-1120

2013

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Learning from incomplete data in Bayesian networks with qualitative influences

International journal of approximate reasoning, 2016-02, Vol.69, p.18-34

2016

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Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks

International journal of approximate reasoning, 2014-10, Vol.55 (7), p.1548-1569

2014

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Scaling up Bayesian variational inference using distributed computing clusters

International journal of approximate reasoning, 2017-09, Vol.88, p.435-451

2017

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Rejoinder on “Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks”

International journal of approximate reasoning, 2014-10, Vol.55 (7), p.1618-1622

2014

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Variational Inference over Nonstationary Data Streams for Exponential Family Models

Mathematics (Basel), 2020-11, Vol.8 (11), p.1942

2020

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Classification with decision trees from a nonparametric predictive inference perspective

Computational statistics & data analysis, 2014-03, Vol.71, p.789-802

2014

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A Bayesian stochastic search method for discovering Markov boundaries

Knowledge-based systems, 2012-11, Vol.35, p.211-223

2012

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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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Bagging Decision Trees on Data Sets with Classification Noise

Foundations of Information and Knowledge Systems, p.248-265

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A Method for Integrating Expert Knowledge When Learning Bayesian Networks From Data

IEEE transactions on systems, man and cybernetics. Part B, Cybernetics, 2011-10, Vol.41 (5), p.1382-1394

2011

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IEEE_Xplore

An Experimental Study about Simple Decision Trees for Bagging Ensemble on Datasets with Classification Noise

Symbolic and Quantitative Approaches to Reasoning with Uncertainty, p.446-456

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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, p.616-628

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An interactive approach for Bayesian network learning using domain/expert knowledge

InferPy: Probabilistic modeling with Tensorflow made easy

Learning Styles Impact Students’ Perceptions on Active Learning Methodologies: A Case Study on the Use of Live Coding and Short Programming Exercises

Probabilistic Models with Deep Neural Networks

An ensemble method using credal decision trees

An Empirical Analysis of the Impact of Continuous Assessment on the Final Exam Mark

Analyzing concept drift: A case study in the financial sector

From anecdote to evidence: the relationship between personality and need for cognition of developers

Bagging schemes on the presence of class noise in classification

InferPy: Probabilistic modeling with deep neural networks made easy

MAP inference in dynamic hybrid Bayesian networks

Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks

New skeleton-based approaches for Bayesian structure learning of Bayesian networks

Learning from incomplete data in Bayesian networks with qualitative influences

Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks

Scaling up Bayesian variational inference using distributed computing clusters

Rejoinder on “Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks”

Variational Inference over Nonstationary Data Streams for Exponential Family Models

Classification with decision trees from a nonparametric predictive inference perspective

A Bayesian stochastic search method for discovering Markov boundaries

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

Bagging Decision Trees on Data Sets with Classification Noise

A Method for Integrating Expert Knowledge When Learning Bayesian Networks From Data

An Experimental Study about Simple Decision Trees for Bagging Ensemble on Datasets with Classification Noise

Virtual Subconcept Drift Detection in Discrete Data Using Probabilistic Graphical Models

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