Results 1 to 10 of about 151,311 (270)
Getting started in probabilistic graphical models. [PDF]
PLoS Computational Biology, 2007 Probabilistic graphical models (PGMs) have become a popular tool for computational analysis of biological data in a variety of domains. But, what exactly are they and how do they work? How can we use PGMs to discover patterns that are biologically relevant?
Edoardo M Airoldi
doaj +7 more sources
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
doaj +2 more sources
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
doaj +3 more sources
Generalized Permutohedra from Probabilistic Graphical Models [PDF]
SIAM Journal on Discrete Mathematics, 2018 Appendix B is expanded. Final version to appear in SIAM J.
Mohammadi, Fatemeh +3 more
openaire +6 more sources
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
doaj +2 more sources
Learning structurally consistent undirected probabilistic graphical models. [PDF]
Proc Int Conf Mach Learn, 2009 In many real-world domains, undirected graphical models such as Markov random fields provide a more natural representation of the statistical dependency structure than directed graphical models. Unfortunately, structure learning of undirected graphs using likelihood-based scores remains difficult because of the intractability of computing the partition
Roy S, Lane T, Werner-Washburne M.
europepmc +4 more sources
Probabilistic Graphical Models [PDF]
, 2014 This report presents probabilistic graphical models that are based on imprecise probabilities using a simplified language. In particular the discussion is focused on credal networks and discrete domains. It describes the building blocks of credal networks algorithms to perform inference and discusses on complexity results and related work.
Antonucci, Alessandro +2 more
+9 more sources
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source