Modeling a Poisson Forest in Variable Elevations: A Nonparametric Bayesian Approach
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Publication:4666638
DOI10.1111/j.0006-341X.1999.00738.xzbMath1059.62743OpenAlexW1981024551WikidataQ52065222 ScholiaQ52065222MaRDI QIDQ4666638
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
reversible jump MCMCnonparametric Bayesian inferencespatial point processspatial interpolationecological response curves
Inference from spatial processes (62M30) Applications of statistics to environmental and related topics (62P12) Nonparametric estimation (62G05) Bayesian inference (62F15)
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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