Efficient and Scalable Approach to Equilibrium Conditional Simulation of Gibbs Markov Random Fields [PDF]
EPJ Web of Conferences, 2020 We study the performance of an automated hybrid Monte Carlo (HMC) approach for conditional simulation of a recently proposed, single-parameter Gibbs Markov random field.
Žukovič Milan +1 more
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The Annals of Applied Probability, 1995 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kunsch, Hans +2 more
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Simulating Lagrangian Subgrid‐Scale Dispersion on Neutral Surfaces in the Ocean
Journal of Advances in Modeling Earth Systems, 2022 To capture the effects of mesoscale turbulent eddies, coarse‐resolution Eulerian ocean models resort to tracer diffusion parameterizations. Likewise, the effect of eddy dispersion needs to be parameterized when computing Lagrangian pathways using coarse ...
Daan Reijnders +2 more
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3D CLASSIFICATION OF CROSSROADS FROM MULTIPLE AERIAL IMAGES USING MARKOV RANDOM FIELDS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012 The precise classification and reconstruction of crossroads from multiple aerial images is a challenging problem in remote sensing. We apply the Markov Random Fields (MRF) approach to this problem, a probabilistic model that can be used to consider ...
S. Kosov +4 more
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Outage Estimation in Electric Power Distribution Systems Using a Neural Network Ensemble
Energies, 2021 Outages in an overhead power distribution system are caused by multiple environmental factors, such as weather, trees, and animal activity. Since they form a major portion of the outages, the ability to accurately estimate these outages is a significant ...
Sanjoy Das +2 more
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Computing the Cramer-Rao bound of Markov random field parameters: Application to the Ising and the Potts models [PDF]
, 2013 This report considers the problem of computing the Cramer-Rao bound for the parameters of a Markov random field. Computation of the exact bound is not feasible for most fields of interest because their likelihoods are intractable and have intractable ...
Batatia, Hadj +3 more
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Bayesian 2D Deconvolution: A Model for Diffuse Ultrasound Scattering [PDF]
Modeling, Identification and Control, 2001 Observed medical ultrasound images are degraded representations of the true acoustic tissue reflectance. The degradation is due to blur and speckle, and significantly reduces the diagnostic value of the images. In order to remove both blur and speckle we
Oddvar Husby +4 more
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Consistent estimation of the basic neighborhood of Markov random fields [PDF]
, 2006 For Markov random fields on $\mathbb{Z}^d$ with finite state space, we address the statistical estimation of the basic neighborhood, the smallest region that determines the conditional distribution at a site on the condition that the values at all other ...
Csiszár, Imre, Talata, Zsolt
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Partially observed Markov random fields are variable neighborhood random fields
, 2012 The present paper has two goals. First to present a natural example of a new class of random fields which are the variable neighborhood random fields. The example we consider is a partially observed nearest neighbor binary Markov random field. The second
Cassandro, Marzio +2 more
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Diffusion Adaptation Strategies for Distributed Estimation over Gaussian Markov Random Fields [PDF]
, 2014 The aim of this paper is to propose diffusion strategies for distributed estimation over adaptive networks, assuming the presence of spatially correlated measurements distributed according to a Gaussian Markov random field (GMRF) model.
Di Lorenzo, Paolo
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