Institutional Repository
Technical University of Crete
Technical University of Crete
EN
|
EL
| URI | http://purl.tuc.gr/dl/dias/59246F3B-3965-4C81-BED7-091E17E29432 | - |
| Identifier | https://doi.org/10.1103/PhysRevE.98.062135 | - |
| Identifier | https://journals.aps.org/pre/abstract/10.1103/PhysRevE.98.062135 | - |
| Language | en | - |
| Extent | 22 pages | en |
| Title | Gibbs Markov random fields with continuous values based on the modified planar rotator model | en |
| Creator | Žukovič, Milan | en |
| Creator | Christopoulos Dionysios | en |
| Creator | Χριστοπουλος Διονυσιος | el |
| Publisher | American Physical Society | en |
| Content Summary | We introduce a Gibbs Markov random field for spatial data on Cartesian grids based on the modified planar rotator (MPR) model of statistical physics. The MPR model captures spatial correlations using nearest-neighbor interactions of continuously valued spins and does not rely on Gaussian assumptions. The only model parameter is the reduced temperature, which we estimate by means of an ergodic specific energy matching principle. We propose an efficient hybrid Monte Carlo simulation algorithm that leads to fast relaxation of the MPR model and allows vectorization. Consequently, the MPR model's computational time for inference and simulation scales approximately linearly with system size. This makes it more suitable for big data sets, such as satellite and radar images, than conventional geostatistical approaches. The performance (accuracy and computational speed) of the MPR model is validated with conditional simulation of Gaussian synthetic and non-Gaussian real data (atmospheric heat release measurements and Walker-lake DEM-based concentrations) and comparisons with standard gap-filling methods. © 2018 American Physical Society. | en |
| Type of Item | Peer-Reviewed Journal Publication | en |
| Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2019-05-21 | - |
| Date of Publication | 2018 | - |
| Subject | Classical statistical mechanics | en |
| Subject | Environmental research | en |
| Subject | Stochastic inference | en |
| Subject | Stochastic processes | en |
| Subject | Classical spin models | en |
| Subject | Equilibrium lattice models | en |
| Subject | Lattice models in statistical physics | en |
| Subject | Computational complexity | en |
| Subject | Data analysis | en |
| Subject | Hybrid Monte Carlo algorithm | en |
| Subject | Markovian processes | en |
| Subject | Metropolis algorithm | en |
| Subject | Monte Carlo methods | en |
| Subject | Spatial modeling | en |
| Subject | Statistical methods | en |
| Subject | Stochastic analysis | en |
| Subject | XY model | en |
| Subject | Gaussian distribution | en |
| Subject | Image segmentation | en |
| Subject | Intelligent systems | en |
| Subject | Markov processes | en |
| Subject | Statistical Physics | en |
| Bibliographic Citation | D.T. Hristopulos and M. Žukovič, "Gibbs Markov random fields with continuous values based on the modified planar rotator model", Phys. Rev. E Stat. Nonlin. Soft Matter Phys., vol. 98, no. 6, Dec. 2018. doi: 10.1103/PhysRevE.98.062135 | en |
Copyright © DIAS 2013
