Bayesian analysis of agricultural field experiments
Bayesian analysis of agricultural field experiments
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DOI：
10.1111/1467-9868.00201
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发表时间：
1999-01-01
影响因子：
5.8
通讯作者：
Higdon, D
中科院分区：
文献类型：
--
作者：
Besag, J;Higdon, D
本文介绍了贝叶斯分析的农业田间试验，一个主题，以前很少受到关注，尽管有大量的频率论文献。贝叶斯范式的采用简化了对结果的解释，特别是在排名和选择方面。此外，可以比较容易地分析复杂的配方。用马尔可夫链蒙特卡罗方法。这一方法的一个关键要素是需要对未观察到的生育模式进行空间表述。这是详细讨论。离群值和生育率跳跃引起的问题通过分层t公式来解决，这些公式可以在其他情况下使用。该文件包括三个品种产量试验的分析和一个涉及二进制数据的例子;没有一个是完全简单的。与频率论分析进行了一些数值比较。
The paper describes Bayesian analysis for agricultural field experiments, a topic that has received very little previous attention, despite a vast frequentist literature. Adoption of the Bayesian paradigm simplifies the interpretation of the results, especially in ranking and selection. Also, complex formulations can be analysed with comparative ease. by using Markov chain Monte Carlo methods. A key ingredient in the approach is the need for spatial representations of the unobserved fertility patterns. This is discussed in detail. Problems caused by outliers and by jumps in fertility are tackled via hierarchical-t formulations that may find use in other contexts. The paper includes three analyses of variety trials for yield and one example involving binary data; none is entirely straightforward. Some numerical comparisons with frequentist analyses are made.