Results 11 to 20 of about 20,022 (282)

Recent advances in probabilistic graphical models [PDF]

Int. J. Intell. Syst., 2015
Probabilistic graphical models constitute a fundamental tool for the development of intelligent systems.
Concha Bielza, Serafín Moral, Antonio Salmerón   +2 more
openaire   +3 more sources

Probabilistic graphical models in artificial intelligence

Applied Soft Computing, 2011
In this paper, we review the role of probabilistic graphical models in artificial intelligence. We start by giving an account of the early years when there was important controversy about the suitability of probability for intelligent systems. We then discuss the main milestones for the foundations of graphical models starting with Pearl’s pioneering ...
Larrañaga Múgica, Pedro María, Moral Callejón, Serafín   +1 more
openaire   +3 more sources

The potential of probabilistic graphical models in linkage map construction. [PDF]

Theor Appl Genet, 2017
Key message: Probabilistic graphical models show great potential for robust and reliable construction of linkage maps. We show how to use probabilistic graphical models to construct high-quality linkage maps in the face of data perturbations caused by ...
Wang H, van Eeuwijk FA, Jansen J.
europepmc   +2 more sources

Perspectives on Probabilistic Graphical Models.

, 2020
QC ...
Liu, Dong
openaire   +3 more sources

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, Xiaoguang Gao, Zidong Wang, Xinxin Ru   +3 more
doaj   +1 more source

Increasing Interpretability of Bayesian Probabilistic Programming Models Through Interactive Representations

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, Sebastian Stein, John H. Williamson   +2 more
doaj   +1 more source

Approximate Learning of High Dimensional Bayesian Network Structures via Pruning of Candidate Parent Sets

Entropy, 2020
Score-based algorithms that learn Bayesian Network (BN) structures provide solutions ranging from different levels of approximate learning to exact learning.
Zhigao Guo, Anthony C. Constantinou
doaj   +1 more source

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

Mathematics, 2022
The computerization of many everyday tasks generates vast amounts of data, and this has lead to the development of machine-learning methods which are capable of extracting useful information from the data so that the data can be used in future decision ...
Pedro Bonilla-Nadal, Andrés Cano, Manuel Gómez-Olmedo, Serafín Moral, Ofelia Paula Retamero   +4 more
doaj   +1 more source

Computation of Kullback–Leibler Divergence in Bayesian Networks

Entropy, 2021
Kullback–Leibler divergence KL(p,q) is the standard measure of error when we have a true probability distribution p which is approximate with probability distribution q.
Serafín Moral, Andrés Cano, Manuel Gómez-Olmedo   +2 more
doaj   +1 more source

Probabilistic knowledge-based characterization of conceptual geological models

Applied Computing and Geosciences, 2021
The construction of conceptual geological models is an essential task in petroleum exploration, especially during the early stages of investment, when evidence about the subsurface is limited.
Júlio Hoffimann, Sandro Rama Fiorini, Breno de Carvalho, Andres Codas, Carlos Raoni, Bianca Zadrozny, Rogério de Paula, Oksana Popova, Maxim Mityaev, Irina Shishmanidi, Anastasiia Gorelova, Zoya Filippova   +11 more
doaj   +1 more source