DragDL:An Easy-to-Use Graphical DL Model Construction System [PDF]
Jisuanji kexue, 2021 Deep learning has broad applications in various fields.However,users still need to face problems from two aspects when applying deep learning.First,deep learning has a complex theoretical background,non-professional users lack background knowledge in ...
TANG Shi-zheng, ZHANG Yan-feng
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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
Shrivastava, Harsh, Chajewska, Urszula
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FastGGM: An Efficient Algorithm for the Inference of Gaussian Graphical Model in Biological Networks
PLoS Comput. Biol., 2016 Biological networks provide additional information for the analysis of human diseases, beyond the traditional analysis that focuses on single variables.
Ting Wang +8 more
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High dimensional semiparametric latent graphical model for mixed data [PDF]
, 2014 We propose a semiparametric latent Gaussian copula model for modelling mixed multivariate data, which contain a combination of both continuous and binary variables. The model assumes that the observed binary variables are obtained by dichotomizing latent
Jianqing Fan, Han Liu, Y. Ning, H. Zou
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Asymptotically Normal and Efficient Estimation of Covariate-Adjusted Gaussian Graphical Model [PDF]
Journal of the American Statistical Association, 2013 We propose an asymptotically normal and efficient procedure to estimate every finite subgraph for covariate-adjusted Gaussian graphical model. As a consequence, a confidence interval as well as p-value can be obtained for each edge.
Mengjie Chen +3 more
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Learning Graphical Model Parameters with Approximate Marginal Inference [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013 Likelihood-based learning of graphical models faces challenges of computational complexity and robustness to model misspecification. This paper studies methods that fit parameters directly to maximize a measure of the accuracy of predicted marginals ...
Justin Domke
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A methodological framework for the morphometric analysis of the fluvial islets along the Danube River in the Giurgiu – Oltenita sector [PDF]
GeoPatterns, 2016 This paper presents a methodology exclusively based on using the Open Source GIS Technology for the morphometric analysis of the fluvial islets along the Danube course.
Andreea-Florentina Marin
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Inferring Differential Networks by Integrating Gene Expression Data With Additional Knowledge
Frontiers in Genetics, 2021 Evidences increasingly indicate the involvement of gene network rewiring in disease development and cell differentiation. With the accumulation of high-throughput gene expression data, it is now possible to infer the changes of gene networks between two ...
Chen Liu +3 more
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Stratified Graphical Models - Context-Specific Independence in Graphical Models
Bayesian Analysis, 2014 19 pages, 7 png figures. In version two the women and mathematics example is replaced with a parliament election data example.
Nyman, Henrik +3 more
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Gaussian graphical model estimation with false discovery rate control [PDF]
, 2013 This paper studies the estimation of high dimensional Gaussian graphical model (GGM). Typically, the existing methods depend on regularization techniques. As a result, it is necessary to choose the regularized parameter. However, the precise relationship
Weidong Liu
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