Enhancing the SPDE modeling of spatial point processes with INLA, applied to wildfires. Choosing the best mesh for each database

DOI10.1080/03610918.2019.1618473zbMath1497.62300OpenAlexW2946827206WikidataQ117713675 ScholiaQ117713675MaRDI QIDQ5082758

Pablo Juan Verdoy

Publication date: 21 June 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10234/183285

zbMATH Keywords

meshBayesian inferencespatial point processINLASPDE

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Random fields; image analysis (62M40) Applications of statistics to environmental and related topics (62P12) Bayesian inference (62F15) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Related Items (2)

Spatio-temporal modeling of traffic accidents incidence on urban road networks based on an explicit network triangulation ⋮ Data-driven chimney fire risk prediction using machine learning and point process tools

Uses Software

• R
• R-Geo
• Stem

Cites Work

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• An Introduction to the Theory of Point Processes
• Integrated nested Laplace approximation for the analysis of count data via the combined model: A simulation study
• INLA or MCMC? A tutorial and comparative evaluation for spatial prediction in log-Gaussian Cox processes
• Spatial and Spatio‐temporal Bayesian Models with R‐INLA
• Bayesian Estimation and Prediction of Stochastic Volatility Models via INLA

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