Results 31 to 40 of about 7,343,069 (347)
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
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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 +4 more
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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 +2 more
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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 +11 more
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Bayesian Test of Significance for Conditional Independence: The Multinomial Model
Entropy, 2014 Conditional independence tests have received special attention lately in machine learning and computational intelligence related literature as an important indicator of the relationship among the variables used by their models.
Pablo de Morais Andrade +2 more
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SIMLR: Machine Learning inside the SIR Model for COVID-19 Forecasting
Forecasting, 2022 Accurate forecasts of the number of newly infected people during an epidemic are critical for making effective timely decisions. This paper addresses this challenge using the SIMLR model, which incorporates machine learning (ML) into the epidemiological ...
Roberto Vega +2 more
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Deep Probabilistic Graphical Modeling
CoRR, 2020 Probabilistic graphical modeling (PGM) provides a framework for formulating an interpretable generative process of data and expressing uncertainty about unknowns, but it lacks flexibility. Deep learning (DL) is an alternative framework for learning from data that has achieved great empirical success in recent years.
openaire +3 more sources
Learning to Generate Posters of Scientific Papers by Probabilistic Graphical Models [PDF]
Journal of Computational Science and Technology, 2017 Researchers often summarize their work in the form of scientific posters. Posters provide a coherent and efficient way to convey core ideas expressed in scientific papers.
Yuting Qiang +5 more
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An Order-Independent Algorithm for Learning Chain Graphs
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2021 LWF chain graphs combine directed acyclic graphs and undirected graphs. We propose a PC-like algorithm, called PC4LWF, that finds the structure of chain graphs under the faithfulness assumption to resolve the problem of scalability of the proposed ...
Mohammad Ali Javidian +2 more
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On a Class of Tensor Markov Fields
Entropy, 2020 Here, we introduce a class of Tensor Markov Fields intended as probabilistic graphical models from random variables spanned over multiplexed contexts. These fields are an extension of Markov Random Fields for tensor-valued random variables.
Enrique Hernández-Lemus
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