Markov random field models for vector-based representations of landscapes

DOI10.1214/21-AOAS1447zbMath1498.62297MaRDI QIDQ2078273

Patrizia Zamberletti, Thomas Opitz, Edith Gabriel, Julien Papaïx

Publication date: 28 February 2022

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

zbMATH Keywords

stochastic geometrypseudo-likelihoodgraphical modelMarkov chain Monte Carlo simulationmultiplex-networkstatistical landscape modeling

Mathematics Subject Classification ID

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

Uses Software

• raster
• Algorithm 447
• FRAGSTATS
• bootlib

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Spatial statistics and modeling. Translated from the French by Kevin Bleakley.
• Pseudolikelihood for exponential family models of spatial point processes
• Resampling methods for dependent data
• Harmony in the small-world
• Markov connected component fields
• Markov Point Processes and Their Applications
• Theory of Spatial Statistics
• Statistics for Spatial Data
• Strictly Proper Scoring Rules, Prediction, and Estimation
• Nearest-Neighbour Markov Point Processes and Random Sets
• ABC likelihood-free methods for model choice in Gibbs random fields