Generating annual fire risk maps using Bayesian hierarchical models

DOI10.1080/15598608.2013.820158zbMath1420.62414OpenAlexW1988955207WikidataQ58034364 ScholiaQ58034364MaRDI QIDQ2320924

P. Pereira, Ana Sá, José M. C. Pereira, M. Antonia Amaral-Turkman, Feridun Turkman

Publication date: 27 August 2019

Published in: Journal of Statistical Theory and Practice (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/15598608.2013.820158

zbMATH Keywords

spatio-temporal modelsdiscrete Weibull modelfire frequency datanon-homogeneous Markov models

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Applications of statistics to environmental and related topics (62P12)

Related Items (2)

Quantification of annual wildfire risk; A spatio-temporal point process approach. ⋮ Generating annual fire risk maps using Bayesian hierarchical models

Uses Software

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• CODA
• WinBUGS
• GMRFLib
• spBayes

Cites Work

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• Asymptotic models and inference for extremes of spatio-temporal data
• Model-based geostatistics.
• Generating annual fire risk maps using Bayesian hierarchical models
• Spatial-temporal rainfall modelling for flood risk estimation
• Structured Spatio-Temporal Shot-Noise Cox Point Process Models, with a View to Modelling Forest Fires
• Nonseparable, Stationary Covariance Functions for Space–Time Data
• Gaussian Markov Random Fields
• Geostatistics of extremes
• Univariate Discrete Distributions

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