BAYESIAN-INFERENCE IN THRESHOLD MODELS USING GIBBS SAMPLING
BAYESIAN-INFERENCE IN THRESHOLD MODELS USING GIBBS SAMPLING
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DOI：
10.1051/gse:19950303
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发表时间：
1995-01-01
影响因子：
4.1
通讯作者：
KORSGAARD, I
中科院分区：
文献类型：
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作者：
SORENSEN, DA;ANDERSEN, S;KORSGAARD, I
给出了一类多有序类别门限模型的贝叶斯分析。边缘化是通过吉布斯采样器实现的。结果表明，使用数据增强导致条件后验分布，这是很容易从采样。阈值和负债的条件后验分布分别是独立的一致分布和独立的截断正态分布。模型的其余参数具有与高斯线性模型中的参数相同的条件后验分布。该方法说明使用父系模型，在狗的髋关节发育不良的分析，并与以前的研究中获得的结果进行比较，基于近似最大似然法。两个独立的吉布斯链的长度620 000。运行，并使用时间序列方法评估后验密度矩的蒙特-卡罗抽样误差。两条链所得结果之间的差异在蒙特-卡罗抽样误差范围内。除了公畜方差和遗传力，边际后验分布似乎正常。因此，使用本方法的推断与基于近似最大似然法的推断具有良好的一致性。在吉布斯序列中，阈值估计具有很强的自相关性，但这可以使用替代参数化来缓解。
A Bayesian analysis of a threshold model with multiple ordered categories is presented. Marginalizations are achieved by means of the Gibbs sampler. It is shown that use of data augmentation leads to conditional posterior distributions which are easy to sample from. The conditional posterior distributions of thresholds and liabilities are independent uniforms and independent truncated normals, respectively. The remaining parameters of the model have conditional posterior distributions which are identical to those in the Gaussian linear model. The methodology is illustrated using a sire model, with an analysis of hip dysplasia in dogs, and the results are compared with those obtained in a previous study, based on approximate maximum likelihood. Two independent Gibbs chains of length 620 000 each were. run, and the Monte-Carlo sampling error of moments of posterior densities were assessed using time series methods. Differences between results obtained from both chains were within the range of the Monte-Carlo sampling error. With the exception of the sire variance and heritability, marginal posterior distributions seemed normal. Hence inferences using the present method were in good agreement with those based on approximate maximum likelihood. Threshold estimates were strongly autocorrelated in the Gibbs sequence, but this can be alleviated using an alternative parameterization.