scholarly journals Spatially Explicit Modeling of Community Occupancy using Markov Random Field Models with Imperfect Observation: Mesocarnivores in Apostle Islands National Lakeshore

2020 ◽  
Author(s):  
Yunyi Shen ◽  
Timothy R. Van Deelen
Keyword(s):  
Markov Random Fields ◽  
Interspecific Interactions ◽  
Positive Association ◽  
Interspecific Interaction ◽  
Spatially Explicit ◽  
Pekania Pennanti ◽  
Markov Random ◽  
Source Sink ◽  
Spatially Explicit Modeling ◽  
Partitioning Pattern

We developed a spatially explicit community occupancy model based on Markov random fields that accounts for spatial auto-correlation and interspecific interactions in occupancy while also accounting for interspecific interaction in detection. Simulation showed the model can distinguish different mechanisms of environmental sorting competition and spatial-autocorrelation. We applied our model to camera trap data from a Fisher(Pekania pennanti)-Marten(Martes americana) and Coyote(Canis latrans)-Fox(Vulpes vulpes) system in Apostle Island National Lakeshore. Results showed the observed partitioning pattern between marten and fisher distributions could be better explained by a flipped mainland-island source-sink pattern rather than competition, while we detected some evidence that on top of the mainland-island source-sink pattern, there was a positive association between fox and coyote that deserved further study.

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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