Predictor selection for streamflows using a graphical modeling approach

2014 ◽  
Vol 29 (6) ◽  
pp. 1583-1599 ◽  
Author(s):  
Meenu Ramadas ◽  
Rajib Maity ◽  
Richa Ojha ◽  
Rao S. Govindaraju
Keyword(s):  
Graphical Modeling ◽  
Modeling Approach ◽  
Selection For

scholarly journals Integrating additional knowledge into the estimation of graphical models

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Yunqi Bu ◽  
Johannes Lederer
Keyword(s):  
Graphical Models ◽  
Tuning Parameter ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Graphical Modeling ◽  
Standard Methods ◽  
Neighborhood Selection ◽  
Selection For ◽  
The Brain

Abstract Graphical models such as brain connectomes derived from functional magnetic resonance imaging (fMRI) data are considered a prime gateway to understanding network-type processes. We show, however, that standard methods for graphical modeling can fail to provide accurate graph recovery even with optimal tuning and large sample sizes. We attempt to solve this problem by leveraging information that is often readily available in practice but neglected, such as the spatial positions of the measurements. This information is incorporated into the tuning parameter of neighborhood selection, for example, in the form of pairwise distances. Our approach is computationally convenient and efficient, carries a clear Bayesian interpretation, and improves standard methods in terms of statistical stability. Applied to data about Alzheimer’s disease, our approach allows us to highlight the central role of lobes in the connectivity structure of the brain and to identify an increased connectivity within the cerebellum for Alzheimer’s patients compared to other subjects.

Super-human multi-talker speech recognition: A graphical modeling approach

2010 ◽  
Vol 24 (1) ◽  
pp. 45-66 ◽  
Author(s):  
John R. Hershey ◽  
Steven J. Rennie ◽  
Peder A. Olsen ◽  
Trausti T. Kristjansson
Keyword(s):  
Speech Recognition ◽  
Graphical Modeling ◽  
Modeling Approach

Semiquantitative SDG Graphical Modeling for Complex System Fault Diagnosis

2012 ◽  
Vol 152-154 ◽  
pp. 1601-1606 ◽  
Author(s):  
Yan Su
Keyword(s):  
Complex System ◽  
Fault Diagnosis ◽  
Failure Analysis ◽  
Supply System ◽  
Data Driven ◽  
Graphical Modeling ◽  
Modeling Methods ◽  
Modeling Approach ◽  
Modeling Process ◽  
Air Supply

For the shortcomings of existing SDG modeling methods in fault diagnosis, a data-driven semi-quantitative SDG automatic graphical modeling approach and a direct manual SDG graphical modeling approach are investigated. Function failure analysis procedures and data modeling process based on system principle are introduced in detail, and relevant graphical modeling tool are developed. A fault diagnosis modeling for the air supply system of certain type of aircraft is taken as an illustration to verify the validity of proposed modeling method.

scholarly journals TOPSIS Multi-Criteria Decision Modeling Approach for Biolubricant Selection for Two-Stroke Petrol Engines

Energies ◽  
2015 ◽  
Vol 8 (12) ◽  
pp. 13960-13970 ◽  
Author(s):  
Masoud Dehghani Soufi ◽  
Barat Ghobadian ◽  
Gholamhassan Najafi ◽  
Mohammad Sabzimaleki ◽  
Talal Yusaf
Keyword(s):  
Decision Modeling ◽  
Modeling Approach ◽  
Selection For

Describing Functioning in People Living With Spinal Cord Injury Across 22 Countries: A Graphical Modeling Approach

2020 ◽  
Vol 101 (12) ◽  
pp. 2112-2143
Author(s):  
Cristina Ehrmann ◽  
Jan D. Reinhardt ◽  
Conran Joseph ◽  
Nazirah Hasnan ◽  
Brigitte Perrouin-Verbe ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Graphical Modeling ◽  
Modeling Approach ◽  
Cord Injury

scholarly journals A Bayesian graphical modeling approach to microRNA regulatory network inference

2010 ◽  
Vol 4 (4) ◽  
pp. 2024-2048 ◽  
Author(s):  
Francesco C. Stingo ◽  
Yian A. Chen ◽  
Marina Vannucci ◽  
Marianne Barrier ◽  
Philip E. Mirkes
Keyword(s):  
Regulatory Network ◽  
Network Inference ◽  
Graphical Modeling ◽  
Modeling Approach
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