scholarly journals A New Inference Algorithm of Dynamic Uncertain Causality Graph Based on Conditional Sampling Method for Complex Cases

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Hao Nie ◽  
Qin Zhang
Keyword(s):  
Sampling Method ◽  
Inference Algorithm ◽  
Conditional Sampling ◽  
Dynamic Uncertain Causality Graph ◽  
Complex Cases

scholarly journals Methodological Development of the Conditional Sampling Method. Part I: Sensitivity to Statistical and Technical Characteristics

2005 ◽  
Vol 114 (3) ◽  
pp. 615-640 ◽  
Author(s):  
A.K. Fotiadi ◽  
F. Lohou ◽  
A. Druilhet ◽  
D. Ser�a ◽  
Y. Brunet ◽  
...  
Keyword(s):  
Sampling Method ◽  
Conditional Sampling ◽  
Methodological Development

scholarly journals A Joint Probability Density–Based Decomposition of Turbulence in the Atmospheric Boundary Layer

2018 ◽  
Vol 146 (2) ◽  
pp. 503-523 ◽  
Author(s):  
Maria J. Chinita ◽  
Georgios Matheou ◽  
João Teixeira
Keyword(s):  
Probability Density ◽  
Vertical Velocity ◽  
Large Scale ◽  
Sampling Method ◽  
Joint Probability ◽  
Convective Cloud ◽  
Conditional Sampling ◽  
Large Eddy Simulation Model ◽  
Joint Probability Density ◽  
Cloud Core

Abstract In convective flows, vertical turbulent fluxes, covariances between vertical velocity and scalar thermodynamic variables, include contributions from local mixing and large-scale coherent motions, such as updrafts and downdrafts. The relative contribution of these motions to the covariance is important in turbulence parameterizations. However, the flux partition is challenging, especially in regions without convective cloud. A method to decompose the vertical flux based on the corresponding joint probability density function (JPD) is introduced. The JPD-based method partitions the full JPD into a joint Gaussian part and the complement, which represent the local mixing and the large-scale coherent motions, respectively. The coherent part can be further divided into updraft and downdraft parts based on the sign of vertical velocity. The flow decomposition is independent of water condensate (cloud) and can be applied in cloud-free convection, the subcloud layer, and stratiform cloud regions. The method is applied to large-eddy simulation model data of three boundary layers. The results are compared with traditional cloud and cloud-core decompositions and a decaying scalar conditional sampling method. The JPD-based method includes a single free parameter and sensitivity tests show weak dependence on the parameter values. The results of the JPD-based method are somewhat similar to the cloud-core and conditional sampling methods. However, differences in the relative magnitude of the flux decomposition terms suggest that an objective definition of the flow regions is subtle and diagnosed flow properties like updraft characteristics depend on the sampling method. Moreover, the flux decomposition depends on the thermodynamic variable and convection characteristics.

An Investigation of the Conditional Sampling Method Used to Estimate Fluxes of Active, Reactive, and Passive Scalars

1996 ◽  
Vol 35 (10) ◽  
pp. 1835-1845 ◽  
Author(s):  
Gabriel G. Katul ◽  
Peter L. Finkelstein ◽  
John F. Clarke ◽  
Thomas G. Ellestad
Keyword(s):  
Sampling Method ◽  
Conditional Sampling ◽  
Passive Scalars

Controls on trace gas exchange observed by a conditional sampling method

1998 ◽  
Vol 32 (19) ◽  
pp. 3265-3274 ◽  
Author(s):  
J.B. Moncrieff ◽  
I.J. Beverland ◽  
D.H. ÓNéill ◽  
F.D. Cropley
Keyword(s):  
Gas Exchange ◽  
Sampling Method ◽  
Trace Gas ◽  
Conditional Sampling

V1390: Laparoscopic Partial Nephrectomy: Technical Considerations and Pitfalls in Complex Cases

2007 ◽  
Vol 177 (4S) ◽  
pp. 458-458
Author(s):  
Erik P. Castle ◽  
Michael E. Woods ◽  
Raju Thomas ◽  
Rodney Davis
Keyword(s):  
Partial Nephrectomy ◽  
Laparoscopic Partial Nephrectomy ◽  
Technical Considerations ◽  
Complex Cases
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