Modeling a Poisson Forest in Variable Elevations: A Nonparametric Bayesian Approach

DOI10.1111/j.0006-341X.1999.00738.xzbMath1059.62743OpenAlexW1981024551WikidataQ52065222 ScholiaQ52065222MaRDI QIDQ4666638

Elja Arjas, Juha Heikkinen

Publication date: 13 April 2005

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.0006-341x.1999.00738.x

zbMATH Keywords

reversible jump MCMCnonparametric Bayesian inferencespatial point processspatial interpolationecological response curves

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Applications of statistics to environmental and related topics (62P12) Nonparametric estimation (62G05) Bayesian inference (62F15)

Related Items

Consistency results on nonparametric Bayesian estimation of level sets using spatial priors, Bayesian spatial modelling of gamma ray count data, Discussion of paper ``Nonparametric Bayesian inference in applications by Peter Müller, Fernando A. Quintana and Garritt L. Page, Model-based clustering of count processes, Bayesian selection of threshold autoregressive models, Bayesian mixture modeling for spatial Poisson process intensities, with applications to extreme value analysis, Adaptive policies for perimeter surveillance problems, Bayesian Partitioning for Estimating Disease Risk, Erlang mixture modeling for Poisson process intensities

Cites Work

• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• An algorithm for nonparametric Bayesian estimation of a Poisson intensity
• Bayesian computation and stochastic systems. With comments and reply.
• Practical Markov Chain Monte Carlo
• Non-parametric Bayesian Estimation of a Spatial Poisson Intensity
• Estimation of Poisson Intensity Using Partially Observed Concomitant Variables
• Bayesian Inference of Survival Probabilities, Under Stochastic Ordering Constraints