Results 1 to 10 of about 152,213 (132)
Mixture-Based Probabilistic Graphical Models for the Label Ranking Problem [PDF]
Entropy, 2021 The goal of the Label Ranking (LR) problem is to learn preference models that predict the preferred ranking of class labels for a given unlabeled instance. Different well-known machine learning algorithms have been adapted to deal with the LR problem. In
Enrique G. Rodrigo +3 more
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Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models
Physical Review X, 2017 Mainstream machine-learning techniques such as deep learning and probabilistic programming rely heavily on sampling from generally intractable probability distributions.
Marcello Benedetti +3 more
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Robust Depth Image Acquisition Using Modulated Pattern Projection and Probabilistic Graphical Models [PDF]
Sensors, 2016 Depth image acquisition with structured light approaches in outdoor environments is a challenging problem due to external factors, such as ambient sunlight, which commonly affect the acquisition procedure.
Jaka Kravanja +4 more
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A review on probabilistic graphical models in evolutionary computation [PDF]
Journal of Heuristics, 2012 Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains.
Pedro Larranaga +2 more
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Getting started in probabilistic graphical models. [PDF]
PLoS Computational Biology, 2007 Edoardo M Airoldi
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Teaching Probabilistic Graphical Models with OpenMarkov
Mathematics, 2022 OpenMarkov is an open-source software tool for probabilistic graphical models. It has been developed especially for medicine, but has also been used to build applications in other fields and for tuition, in more than 30 countries.
Francisco Javier Díez +3 more
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Strengthening Probabilistic Graphical Models: The Purge-and-Merge Algorithm
IEEE Access, 2021 Probabilistic graphical models (PGMs) are powerful tools for solving systems of complex relationships over a variety of probability distributions. However, while tree-structured PGMs always result in efficient and exact solutions, inference on graph (or ...
Simon Streicher, Johan A. Du Preez
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The Role of Instrumental Variables in Causal Inference Based on Independence of Cause and Mechanism
Entropy, 2021 Causal inference methods based on conditional independence construct Markov equivalent graphs and cannot be applied to bivariate cases. The approaches based on independence of cause and mechanism state, on the contrary, that causal discovery can be ...
Nataliya Sokolovska +1 more
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Improved Local Search with Momentum for Bayesian Networks Structure Learning
Entropy, 2021 Bayesian Networks structure learning (BNSL) is a troublesome problem that aims to search for an optimal structure. An exact search tends to sacrifice a significant amount of time and memory to promote accuracy, while the local search can tackle complex ...
Xiaohan Liu +3 more
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Frontiers in Computer Science, 2020 Bayesian probabilistic modeling is supported by powerful computational tools like probabilistic programming and efficient Markov Chain Monte Carlo (MCMC) sampling. However, the results of Bayesian inference are challenging for users to interpret in tasks
Evdoxia Taka +2 more
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