Results 21 to 30 of about 151,404 (250)
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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Marginal and simultaneous predictive classification using stratified graphical models [PDF]
, 2014 An inductive probabilistic classification rule must generally obey the principles of Bayesian predictive inference, such that all observed and unobserved stochastic quantities are jointly modeled and the parameter uncertainty is fully acknowledged ...
Corander, Jukka +3 more
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A review on probabilistic graphical models in evolutionary computation [PDF]
, 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.
A. Brownlee +112 more
core +2 more sources
From Probabilistic Graphical Models to Generalized Tensor Networks for Supervised Learning
IEEE Access, 2020 Tensor networks have found a wide use in a variety of applications in physics and computer science, recently leading to both theoretical insights as well as practical algorithms in machine learning.
Ivan Glasser +2 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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Recent advances in imprecise-probabilistic graphical models [PDF]
, 2012 We summarise and provide pointers to recent advances in inference and identification for specific types of probabilistic graphical models using imprecise probabilities.
De Bock, Jasper +2 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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Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons. [PDF]
PLoS Computational Biology, 2011 An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in ...
Dejan Pecevski +2 more
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Directed expected utility networks [PDF]
, 2016 A variety of statistical graphical models have been defined to represent the conditional independences underlying a random vector of interest. Similarly, many different graphs embedding various types of preferential independences, such as, for example ...
Leonelli, Manuele, Smith, Jim Q.
core +3 more sources