Results 41 to 50 of about 7,343,069 (347)
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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Learning Semantic Models of Data Sources Using Probabilistic Graphical Models
The Web Conference, 2019 A semantic model of a data source is a representation of the concepts and relationships contained in the data. Building semantic models is a prerequisite to automatically publishing data to a knowledge graph.
B. Vu, Craig A. Knoblock, J. Pujara
semanticscholar +1 more source
Bayesian graphical models for computational network biology
BMC Bioinformatics, 2018 Background Computational network biology is an emerging interdisciplinary research area. Among many other network approaches, probabilistic graphical models provide a comprehensive probabilistic characterization of interaction patterns between molecules ...
Yang Ni, Peter Müller, Lin Wei, Yuan Ji
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Probabilistic Graphical Models on Multi-Core CPUs Using Java 8 [PDF]
IEEE Computational Intelligence Magazine, 2016 In this paper, we discuss software design issues related to the development of parallel computational intelligence algorithms on multi-core CPUs, using the new Java 8 functional programming features.
Andrés R. Masegosa +2 more
semanticscholar +1 more source
Approximate inference in graphical models [PDF]
, 2010 Probability theory provides a mathematically rigorous yet conceptually flexible calculus of uncertainty, allowing the construction of complex hierarchical models for real-world inference tasks.
Hennig, P.
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Regularized estimation of large-scale gene association networks using graphical Gaussian models [PDF]
, 2009 Graphical Gaussian models are popular tools for the estimation of (undirected) gene association networks from microarray data. A key issue when the number of variables greatly exceeds the number of samples is the estimation of the matrix of partial ...
Schäfer, Juliane +10 more
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OpenMarkov, an Open-Source Tool for Probabilistic Graphical Models
International Joint Conference on Artificial Intelligence, 2019 OpenMarkov is a Java open-source tool for creating and evaluating probabilistic graphical models, including Bayesian networks, influence diagrams, and some Markov models.
M. Arias +3 more
semanticscholar +1 more source
Generalized Permutohedra from Probabilistic Graphical Models [PDF]
SIAM Journal on Discrete Mathematics, 2016 A graphical model encodes conditional independence relations via the Markov properties. For an undirected graph these conditional independence relations can be represented by a simple polytope known as the graph associahedron, which can be constructed as
F. Mohammadi +3 more
semanticscholar +1 more source
Probabilistic Graphical Model Representation in Phylogenetics [PDF]
Systematic Biology, 2014 Recent years have seen a rapid expansion of the model space explored in statistical phylogenetics, emphasizing the need for new approaches to statistical model representation and software development. Clear communication and representation of the chosen model is crucial for: (1) reproducibility of an analysis, (2) model development and (3) software ...
Höhna, Sebastian +5 more
openaire +4 more sources
A graphical framework for representing the semantics of asymmetric models [PDF]
, 2005 Bayesian networks (BNs) are useful for coding conditional independence statements, especially in discrete symmetric models. On the other hand, event trees (ETs) are convenient for representing asymmetric structure and how situations unfold.
Smith, J. Q., Anderson, Paul E.
core