Results 61 to 70 of about 22,868,572 (367)
Mathematics, 2022 Strong exogeneity is an important assumption in the study of causal inference, but it is difficult to identify according to its definition. The twin network method provides a graphical model tool for analyzing the variable relationship, involving the ...
Rui Luo +4 more
doaj +1 more source
Too Many Cooks: Exploring How Graphical Perception Studies Influence Visualization Recommendations in Draco [PDF]
, 2023 Findings from graphical perception can guide visualization recommendation algorithms in identifying effective visualization designs. However, existing algorithms use knowledge from, at best, a few studies, limiting our understanding of how complementary (or contradictory) graphical perception results influence generated recommendations.
arxiv +1 more source
GRAPHICAL MODELS FOR CORRELATED DEFAULTS [PDF]
Mathematical Finance, 2012 A simple graphical model for correlated defaults is proposed, with explicit formulas for the loss distribution. Algebraic geometry techniques are employed to show that this model is well posed for default dependence: it represents any given marginal distribution for single firms and pairwise correlation matrix.
I. Onur Filiz +3 more
openaire +3 more sources
Axioms, 2022 This paper shows a visual analysis and the dependence relationships of COVID-19 mortality data in 50 states plus Washington, D.C., from January 2020 to 1 September 2022.
Jong-Min Kim
doaj +1 more source
Alternating Direction Methods for Latent Variable Gaussian Graphical Model Selection [PDF]
Neural Computation, 2012 Chandrasekaran, Parrilo, and Willsky (2012) proposed a convex optimization problem for graphical model selection in the presence of unobserved variables.
Shiqian Ma, Lingzhou Xue, H. Zou
semanticscholar +1 more source
Conditional Functional Graphical Models
Journal of the American Statistical Association, 2021 Graphical modeling of multivariate functional data is becoming increasingly important in a wide variety of applications. The changes of graph structure can often be attributed to external variables, such as the diagnosis status or time, the latter of which gives rise to the problem of dynamic graphical modeling.
Hongyu Zhao +4 more
openaire +3 more sources
Efficient Localized Inference for Large Graphical Models
, 2017 We propose a new localized inference algorithm for answering marginalization queries in large graphical models with the correlation decay property. Given a query variable and a large graphical model, we define a much smaller model in a local region ...
Chen, Jinglin, Liu, Qiang, Peng, Jian
core +1 more source
Heterogeneous Reciprocal Graphical Models [PDF]
Biometrics, 2017 Summary We develop novel hierarchical reciprocal graphical models to infer gene networks from heterogeneous data. In the case of data that can be naturally divided into known groups, we propose to connect graphs by introducing a hierarchical prior across group-specific graphs, including a correlation on edge strengths across graphs ...
Yang Ni +4 more
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On graphical models and convex geometry
Computational Statistics & Data Analysis, 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, Haim, Wells, Martin T.
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An Interdisciplinary Model for Graphical Representation [PDF]
, 2021 The paper questions whether data-driven and problem-driven models are sufficient for a software to automatically represent a meaningful graphi-cal representation of scientific findings. The paper presents descriptive and prescriptive case studies to understand the benefits and the shortcomings of existing models that aim to provide graphical ...
Pierro, Giuseppe Antonio +3 more
openaire +4 more sources