scholarly journals Approximate Bayesian Analysis of Spatio-Temporal Data Using a Gaussian Markov Random Field

2017 ◽  
Vol 10 (2) ◽  
pp. 233-260
Author(s):  
Fatemeh Hosseini ◽  
Elham Homayonfal ◽  
◽  
Keyword(s):  
Bayesian Analysis ◽  
Random Field ◽  
Markov Random Field ◽  
Temporal Data ◽  
Gaussian Markov Random Field ◽  
Markov Random ◽  
Spatio Temporal ◽  
Approximate Bayesian

fMRI Signal Restoration Using a Spatio-Temporal Markov Random Field Preserving Transitions

NeuroImage ◽  
1998 ◽  
Vol 8 (4) ◽  
pp. 340-349 ◽  
Author(s):  
Xavier Descombes ◽  
Frithjof Kruggel ◽  
D.Yves von Cramon
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Signal Restoration ◽  
Fmri Signal ◽  
Markov Random ◽  
Spatio Temporal

Risks of Classification of the Gaussian Markov Random Field Observations

2018 ◽  
Vol 35 (3) ◽  
pp. 422-436 ◽  
Author(s):  
Kęstutis Dučinskas ◽  
Lina Dreižienė
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Field Observations ◽  
Gaussian Markov Random Field ◽  
Markov Random

scholarly journals Mobile Robotic Sensors for Environmental Monitoring using Gaussian Markov Random Field

Robotica ◽  
2020 ◽  
pp. 1-23
Author(s):  
Linh Nguyen ◽  
Sarath Kodagoda ◽  
Ravindra Ranasinghe ◽  
Gamini Dissanayake
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Adaptive Sampling ◽  
Published Data ◽  
Data Sets ◽  
Independence Property ◽  
Gaussian Markov Random Field ◽  
Markov Random ◽  
Sampling Optimization ◽  
Robotic Sensors

SUMMARY This paper addresses the issue of monitoring spatial environmental phenomena of interest utilizing information collected by a network of mobile, wireless, and noisy sensors that can take discrete measurements as they navigate through the environment. It is proposed to employ Gaussian Markov random field (GMRF) represented on an irregular discrete lattice by using the stochastic partial differential equations method to model the physical spatial field. It then derives a GMRF-based approach to effectively predict the field at unmeasured locations, given available observations, in both centralized and distributed manners. Furthermore, a novel but efficient optimality criterion is then proposed to design centralized and distributed adaptive sampling strategies for the mobile robotic sensors to find the most informative sampling paths in taking future measurements. By taking advantage of conditional independence property in the GMRF, the adaptive sampling optimization problem is proven to be resolved in a deterministic time. The effectiveness of the proposed approach is compared and demonstrated using pre-published data sets with appealing results.

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