Investigation of the widely applicable Bayesian information criterion
Investigation of the widely applicable Bayesian information criterion
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DOI：
10.1007/s11222-016-9657-y
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发表时间：
2017-05-01
影响因子：
2.2
通讯作者：
Pettitt, A. N.
中科院分区：
文献类型：
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作者：
Friel, N.;McKeone, J. P.;Pettitt, A. N.
广泛适用的贝叶斯信息准则（WBIC）是一个简单而快速的近似模型的证据，已收到很少的实际考虑。WBIC使用这样一个事实，即对数证据可以被写为对数偏差的期望，关于与提高到幂的可能性成比例的幂后验。找到这个温度值通常是一个棘手的问题。我们发现，对于一个特定的易于处理的统计模型，具有正确温度的WBIC的最佳调谐版本的均方误差低于热力学积分（功率后验）的最佳调谐版本。然而，在实践中，WBIC使用的规范选择。在这里，我们调查WBIC在实践中的性能，一系列的统计模型，包括常规模型和奇异模型，如潜变量模型或具有层次结构的BIC不能提供足够的解决方案。我们的研究结果是，一般WBIC充分执行时，使用信息先验，但它可以系统地高估的证据，特别是小样本量。
The widely applicable Bayesian information criterion (WBIC) is a simple and fast approximation to the model evidence that has received little practical consideration. WBIC uses the fact that the log evidence can be written as an expectation, with respect to a powered posterior proportional to the likelihood raised to a power , of the log deviance. Finding this temperature value is generally an intractable problem. We find that for a particular tractable statistical model that the mean squared error of an optimally-tuned version of WBIC with correct temperature is lower than an optimally-tuned version of thermodynamic integration (power posteriors). However in practice WBIC uses the a canonical choice of . Here we investigate the performance of WBIC in practice, for a range of statistical models, both regular models and singular models such as latent variable models or those with a hierarchical structure for which BIC cannot provide an adequate solution. Our findings are that, generally WBIC performs adequately when one uses informative priors, but it can systematically overestimate the evidence, particularly for small sample sizes.