Linking physics and spatial statistics: a new family of Boltzmann-Gibbs random fields

DOI10.1214/21-EJS1879zbMath1471.62478MaRDI QIDQ2233567

Dionisios T. Hristopulos, Thomas Opitz, Denis Allard

Publication date: 11 October 2021

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-15/issue-2/Linking-physics-and-spatial-statistics-A-new-family-of/10.1214/21-EJS1879.full

zbMATH Keywords

predictionestimationregularizationspatial covarianceBesag processLeroux process

Mathematics Subject Classification ID

Random fields (60G60) Inference from spatial processes (62M30) Random fields; image analysis (62M40) Applications of statistics to environmental and related topics (62P12) Interacting random processes; statistical mechanics type models; percolation theory (60K35) Applications of statistics to physics (62P35)

Related Items (1)

Boltzmann–Gibbs Random Fields with Mesh-free Precision Operators Based on Smoothed Particle Hydrodynamics

Uses Software

• Matlab
• Gstat
• GMRFLib
• gstat

Cites Work

• An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
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• Geostatistics
• Analytic Properties and Covariance Functions for a New Class of Generalized Gibbs Random Fields
• Spartan Gibbs Random Field Models for Geostatistical Applications
• Gaussian Markov Random Fields
• Estimating Space and Space-Time Covariance Functions for Large Data Sets: A Weighted Composite Likelihood Approach
• Spectral density estimation for random fields via periodic embeddings
• Beitrag zur Theorie des Ferromagnetismus
• Approximate Likelihood for Large Irregularly Spaced Spatial Data
• Statistical Physics of Fields
• The Monte Carlo Method
• ON STATIONARY PROCESSES IN THE PLANE
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