scholarly journals Bayesian Graphical Models for Discrete Data

1995 ◽  
Vol 63 (2) ◽  
pp. 215 ◽  
Author(s):  
David Madigan ◽  
Jeremy York ◽  
Denis Allard
Keyword(s):  
Graphical Models ◽  
Discrete Data ◽  
Bayesian Graphical Models

scholarly journals Bayesian graphical models for modern biological applications

2021 ◽  
Author(s):  
Yang Ni ◽  
Veerabhadran Baladandayuthapani ◽  
Marina Vannucci ◽  
Francesco C. Stingo
Keyword(s):  
Graphical Models ◽  
Cancer Genomics ◽  
Graph Structure ◽  
Biological Processes ◽  
Limited Sample ◽  
Large Networks ◽  
Bayesian Approaches ◽  
Complex Sampling ◽  
Complex Dependence ◽  
Bayesian Graphical Models

AbstractGraphical models are powerful tools that are regularly used to investigate complex dependence structures in high-throughput biomedical datasets. They allow for holistic, systems-level view of the various biological processes, for intuitive and rigorous understanding and interpretations. In the context of large networks, Bayesian approaches are particularly suitable because it encourages sparsity of the graphs, incorporate prior information, and most importantly account for uncertainty in the graph structure. These features are particularly important in applications with limited sample size, including genomics and imaging studies. In this paper, we review several recently developed techniques for the analysis of large networks under non-standard settings, including but not limited to, multiple graphs for data observed from multiple related subgroups, graphical regression approaches used for the analysis of networks that change with covariates, and other complex sampling and structural settings. We also illustrate the practical utility of some of these methods using examples in cancer genomics and neuroimaging.

Bayesian Graphical Models

2016 ◽  
pp. 1-9
Author(s):  
Finn V. Jensen ◽  
Thomas D. Nielsen
Keyword(s):  
Graphical Models ◽  
Bayesian Graphical Models

Bayesian Graphical Models for High-Complexity Testing

2018 ◽  
pp. 213-243
Author(s):  
David Wooff ◽  
Michael Goldstein ◽  
Frank Coolen
Keyword(s):  
Graphical Models ◽  
High Complexity ◽  
Bayesian Graphical Models

Application of Bayesian Graphical Models to Iris Recognition

2013 ◽  
pp. 381-398
Author(s):  
B.V.K. Vijaya Kumar ◽  
Vishnu Naresh Boddeti ◽  
Jonathon M. Smereka ◽  
Jason Thornton ◽  
Marios Savvides
Keyword(s):  
Graphical Models ◽  
Iris Recognition ◽  
Bayesian Graphical Models

Bayesian Graphical Models for Integrating Multiplatform Genomics Data

2013 ◽  
pp. 292-311
Author(s):  
Wenting Wang ◽  
Veerabhadran Baladandayuthapani ◽  
Chris C. Holmes ◽  
Kim-Anh Do
Keyword(s):  
Graphical Models ◽  
Bayesian Graphical Models

scholarly journals Estimating heterogeneous graphical models for discrete data with an application to roll call voting

2015 ◽  
Vol 9 (2) ◽  
pp. 821-848 ◽  
Author(s):  
Jian Guo ◽  
Jie Cheng ◽  
Elizaveta Levina ◽  
George Michailidis ◽  
Ji Zhu
Keyword(s):  
Graphical Models ◽  
Discrete Data ◽  
Roll Call ◽  
Roll Call Voting

Familial tendency to foetal loss analysed with Bayesian graphical models by Gibbs sampling

2000 ◽  
Vol 19 (16) ◽  
pp. 2147-2168 ◽  
Author(s):  
Heidi H. Hundborg ◽  
Malene Højbjerre ◽  
Ole Bjarne Christiansen ◽  
Steffen L. Lauritzen
Keyword(s):  
Gibbs Sampling ◽  
Graphical Models ◽  
Foetal Loss ◽  
Familial Tendency ◽  
Bayesian Graphical Models
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