Stochastic jump-diffusion process for computing medial axes in Markov random fields

1999 ◽  
Vol 21 (11) ◽  
pp. 1158-1169 ◽  
Author(s):  
Song-Chun Zhu
Keyword(s):  
Diffusion Process ◽  
Random Fields ◽  
Markov Random Fields ◽  
Jump Diffusion ◽  
Markov Random ◽  
Stochastic Jump ◽  
Jump Diffusion Process

scholarly journals Determination of the carbon market incremental payoff considering a stochastic jump-diffusion process

2013 ◽  
Vol 33 (3) ◽  
pp. 361-382
Author(s):  
Fabio Rodrigo Siqueira Batista ◽  
Tara Keshar Nanda Baidya ◽  
José Paulo Teixeira ◽  
Albert Cordeiro Geber Melo
Keyword(s):  
Diffusion Process ◽  
Jump Diffusion ◽  
Carbon Market ◽  
Stochastic Jump ◽  
Jump Diffusion Process

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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