On large deviations and relative entropy of Markov random fields

2006 ◽  
pp. 27-32
Author(s):  
H. Föllmer
Keyword(s):  
Large Deviations ◽  
Relative Entropy ◽  
Random Fields ◽  
Markov Random Fields ◽  
Markov Random

Mathematics and Innovation in Engineering

2008 ◽  
Vol 380 ◽  
pp. 3-14
Author(s):  
Elena Beretta ◽  
Alberto Gandolfi ◽  
C.C.A. Sastri
Keyword(s):  
Markov Chains ◽  
Large Deviations ◽  
Random Fields ◽  
Spin Glasses ◽  
Markov Random Fields ◽  
Imaging Technologies ◽  
Inverse Conductivity Problem ◽  
Markov Random ◽  
The Way ◽  
Conductivity Problem

We present some examples of mathematical discoveries whose original import was mainly theoretical but which later ended up triggering extraordinary ad- vances in engineering, sometimes all the way down to technological realizations and market products. The examples we cite include Markov chains and Markov random fields, spin glasses, large deviations and the inverse conductivity problem, and their effects in various areas such as communication and imaging technologies.

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.

Optimal smoothing for spherical Gauss–Markov Random Fields with application to weather data estimation

2017 ◽  
Vol 33 ◽  
pp. 43-51 ◽  
Author(s):  
Alessandro Borri ◽  
Francesco Carravetta ◽  
Langford B. White
Keyword(s):  
Random Fields ◽  
Markov Random Fields ◽  
Weather Data ◽  
Markov Random ◽  
Optimal Smoothing ◽  
Gauss Markov Random Fields ◽  
Data Estimation
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