Bayesian Prediction in Clipped GLG Random Field Using Slice Sampling

DOI

10.2991/jsta.2014.13.2.5How to use a DOI?

Keywords

Binary spatial data; Bayesian latent model; Heavy tail; Slice sampling

Abstract

By assuming that an underlying Gaussian-Log Gaussian (GLG) random field clipped to yield binary spatial data, we propose a new model which provides flexibility in capturing the effects of heavy tail in latent variables. For our analysis, we adopt a Bayesian framework and develop a Markov chain Monte Carlo (MCMC) algorithm to carry out the posterior computations. Specifically, we introduce auxiliary variables and employ the slice sampling method to simulate from the full conditional distribution of components which does not define a standard probability distribution. Then, the predictive distribution at unsampled sites is approximated based on acquired samples. Finally, we illustrate our methodology considering simulation and real data sets.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Majid Jafari Khaledi
AU  - Hamidreza Zareifard
AU  - Firoozeh Rivaz
PY  - 2014
DA  - 2014/03/31
TI  - Bayesian Prediction in Clipped GLG Random Field Using Slice Sampling
JO  - Journal of Statistical Theory and Applications
SP  - 151
EP  - 161
VL  - 13
IS  - 2
SN  - 2214-1766
UR  - https://doi.org/10.2991/jsta.2014.13.2.5
DO  - 10.2991/jsta.2014.13.2.5
ID  - Khaledi2014
ER  -