Default priors for Gaussian processes
Default priors for Gaussian processes
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DOI：
10.1214/009053604000001264
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发表时间：
2005-04-01
影响因子：
4.5
通讯作者：
Paulo, R
中科院分区：
文献类型：
--
作者：
Paulo, R
受复杂计算机模型统计评估的推动，我们处理高斯过程参数的客观先验指定问题。特别是，我们针对这种情况推导了杰弗里斯规则、独立性杰弗里斯和参考先验，并证明所得到的后验分布在一组相当一般的条件下是正确的。还考虑了基于最大似然估计的适当的平坦先验策略，然后根据随后的贝叶斯过程的频率属性对所有先验进行比较。本文还讨论了计算问题，并通过复杂计算机模型验证领域的示例来说明所提出的解决方案。
Motivated by the statistical evaluation of complex computer models, we deal with the issue of objective prior specification for the parameters of Gaussian processes. In particular, we derive the Jeffreys-rule, independence Jeffreys and reference priors for this situation, and prove that the resulting posterior distributions are proper under a quite general set of conditions. A proper flat prior strategy, based on maximum likelihood estimates, is also considered, and all priors are then compared on the grounds of the frequentist properties of the ensuing Bayesian procedures. Computational issues are also addressed in the paper, and we illustrate the proposed solutions by means of an example taken from the field of complex computer model validation.