Results 11 to 20 of about 317,964 (264)
On Graphical Models and Convex Geometry. [PDF]
Comput Stat Data Anal, 2023 We introduce a mixture-model of beta distributions to identify significant correlations among $P$ predictors when $P$ is large. The method relies on theorems in convex geometry, which we use to show how to control the error rate of edge detection in graphical models. Our `betaMix' method does not require any assumptions about the network structure, nor
Bar H, Wells MT.
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On Sufficient Graphical Models
CoRR, 2023 We introduce a sufficient graphical model by applying the recently developed nonlinear sufficient dimension reduction techniques to the evaluation of conditional independence. The graphical model is nonparametric in nature, as it does not make distributional assumptions such as the Gaussian or copula Gaussian assumptions.
Bing Li, Kyongwon Kim
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Transforming Graphical System Models to Graphical Attack Models [PDF]
, 2016 Manually identifying possible attacks on an organisation is a complex undertaking; many different factors must be considered, and the resulting attack scenarios can be complex and hard to maintain as the organisation changes. System models provide a systematic representation of organisations that helps in structuring attack identification and can ...
Marieta Georgieva Ivanova +3 more
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, 2023 Probabilistic Graphical Models are often used to understand dynamics of a system. They can model relationships between features (nodes) and the underlying distribution. Theoretically these models can represent very complex dependency functions, but in practice often simplifying assumptions are made due to computational limitations associated with graph
Harsh Shrivastava 0001 +1 more
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J. Mach. Learn. Res., 2014 Stable random variables are motivated by the central limit theorem for densities with (potentially) unbounded variance and can be thought of as natural generalizations of the Gaussian distribution to skewed and heavy-tailed phenomenon. In this paper, we introduce stable graphical (SG) models, a class of multivariate stable densities that can also be ...
Misra N, Kuruoglu E E
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Sum–product graphical models [PDF]
Machine Learning, 2019 This paper introduces a new probabilistic architecture called Sum-Product Graphical Model (SPGM). SPGMs combine traits from Sum-Product Networks (SPNs) and Graphical Models (GMs): Like SPNs, SPGMs always enable tractable inference using a class of models that incorporate context specific independence.
Mattia Desana, Christoph Schnörr
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MULTIAGENT EXPEDITION WITH GRAPHICAL MODELS [PDF]
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2011 We investigate a class of multiagent planning problems termed multiagent expedition, where agents move around an open, unknown, partially observable, stochastic, and physical environment, in pursuit of multiple and alternative goals of different utility. Optimal planning in multiagent expedition is highly intractable.
Xiang, Yang, Hanshar, Frank
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A Tighter Bound for Graphical Models [PDF]
Neural Computation, 2001 We present a method to bound the partition function of a Boltzmann machine neural network with any odd-order polynomial. This is a direct extension of the mean-field bound, which is first order. We show that the third-order bound is strictly better than mean field.
Leisink, M.A.R., Kappen, H.J.
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Lifted graphical models: a survey [PDF]
Machine Learning, 2014 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kimmig, Angelika +2 more
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, 2016 Structured high-cardinality data arises in many domains, and poses a major challenge for both modeling and inference. Graphical models are a popular approach to modeling structured data but they are unsuitable for high-cardinality variables. The count-min (CM) sketch is a popular approach to estimating probabilities in high-cardinality data but it does
Branislav Kveton +5 more
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