Slice sampling for simulation based fitting of spatial data models

DEEPAK K. AGARWAL; ALAN E. GELFAND

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Slice sampling for simulation based fitting of spatial data models

机译：切片采样，用于基于模拟的空间数据模型拟合

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An auxiliary variable method based on a slice sampler is shown to provide an attractive simulation-based model fitting strategy for fitting Bayesian models under proper priors. Though broadly applicable, we illustrate in the context of fitting spatial models for geo-referenced or point source data. Spatial modeling within a Bayesian framework offers inferential advantages and the slice sampler provides an algorithm which is essentially "off the shelf. Further potential advantages over importance sampling approaches and Metropolis approaches are noted and illustrative examples are supplied.

机译：示出了基于切片采样器的辅助变量方法，该方法提供了一种有吸引力的基于仿真的模型拟合策略，用于在适当的先验条件下拟合贝叶斯模型。尽管广泛适用，但我们在为地理参考或点源数据拟合空间模型的背景下进行了说明。贝叶斯框架内的空间建模提供了推论优势，而切片采样器提供了一种实质上“现成的”算法。与重要性采样方法和Metropolis方法相比，潜在的其他优势也被提及，并提供了示例性示例。

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来源
《Statistics and computing》 |2005年第1期|p.61-69|共9页
作者
DEEPAK K. AGARWAL; ALAN E. GELFAND;
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作者单位

Senior Technical Staff Member, AT&TShannon Labs, Florham Park, NJ 07932-0791, USA;

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原文格式 PDF
正文语种 eng
中图分类统计学;
关键词
auxiliary variables; bayesian modeling; exponential and matern covariance functions; stationary gaussian process;

机译：辅助变量;贝叶斯建模;指数和矩阵协方差函数;平稳高斯过程;

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Slice sampling for simulation based fitting of spatial data models

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