scholarly journals Revealing the Tectonic Structure of the Bay of Iskenderun Using the Markov Random Fields Method

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Ali muhittin ALBORA
Keyword(s):  
Magnetic Anomaly ◽  
Random Fields ◽  
Markov Random Fields ◽  
Markov Random ◽  
Gravity And Magnetic ◽  
Iskenderun Bay ◽  
Stochastic Characteristic ◽  
Fault Lines ◽  
Anomaly Map

One of the important issues in Gravity and Magnetic prospecting is to separate regional and residual anomalies. Determination of this separation between them is also crucial. Markov Random Fields (MRF) method is used to distinguish these anomalies. The most important property of this method is to make use of stochastic characteristic in neighborhood and 2D view. MRF does not need any training before use. This method is first applied to magnetic anomaly map consisting of prisms. After application, MRF method gives satisfactory results. We use Iskenderun Bay magnetic anomaly map to determined off-shore fault lines. According to our results, we reveal fault lines along Iskenderun Bay.

scholarly journals Incorporating interaction networks into the determination of functionally related hit genes in genomic experiments with Markov random fields

2017 ◽  
Vol 33 (14) ◽  
pp. i170-i179 ◽  
Author(s):  
Sean Robinson ◽  
Jaakko Nevalainen ◽  
Guillaume Pinna ◽  
Anna Campalans ◽  
J Pablo Radicella ◽  
...  
Keyword(s):  
Random Fields ◽  
Markov Random Fields ◽  
Interaction Networks ◽  
Markov Random

Sampling from Gaussian Markov random fields conditioned on linear constraints

2008 ◽  
Vol 48 ◽  
pp. 1041 ◽  
Author(s):  
Daniel Peter Simpson ◽  
Ian W. Turner ◽  
A. N. Pettitt
Keyword(s):  
Random Fields ◽  
Markov Random Fields ◽  
Linear Constraints ◽  
Gaussian Markov Random Fields ◽  
Markov Random

scholarly journals A Methodology for Redesigning Networks by Using Markov Random Fields

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1389
Author(s):  
Julia García Cabello ◽  
Pedro A. Castillo ◽  
Maria-del-Carmen Aguilar-Luzon ◽  
Francisco Chiclana ◽  
Enrique Herrera-Viedma
Keyword(s):  
Decision Making ◽  
Random Fields ◽  
Markov Random Fields ◽  
Application Performance ◽  
Cross Border ◽  
Markov Random ◽  
User Groups ◽  
Universal Definition ◽  
Definition Of ◽  
Decision Making Model

Standard methodologies for redesigning physical networks rely on Geographic Information Systems (GIS), which strongly depend on local demographic specifications. The absence of a universal definition of demography makes its use for cross-border purposes much more difficult. This paper presents a Decision Making Model (DMM) for redesigning networks that works without geographical constraints. There are multiple advantages of this approach: on one hand, it can be used in any country of the world; on the other hand, the absence of geographical constraints widens the application scope of our approach, meaning that it can be successfully implemented either in physical (ATM networks) or non-physical networks such as in group decision making, social networks, e-commerce, e-governance and all fields in which user groups make decisions collectively. Case studies involving both types of situations are conducted in order to illustrate the methodology. The model has been designed under a data reduction strategy in order to improve application performance.

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