On asymptotic validity of naive inference with an approximate likelihood
On asymptotic validity of naive inference with an approximate likelihood
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近似似然朴素推理的渐近有效性
DOI：
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发表时间：
2016
期刊：
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通讯作者：
Helen E. Ogden
中科院分区：
文献类型：
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作者：
Helen E. Ogden
&na；许多统计模型都有难以处理的可能性：准确计算可能性是不可能的，或者成本太高。在这种情况下，一种常见的方法是用近似值代替似然，并进行推理，就好像近似似然就是真似然一样。在这篇文章中，我们描述了保证这种近似似然的朴素推理与真实似然推理具有相同的一阶渐近性质的条件。我们使用一个简单的两级潜变量模型中的似然的拉普拉斯近似和一个伊辛模型中的似然的降低的依赖近似来研究这些结果对于推断的意义。
&NA; Many statistical models have likelihoods which are intractable: it is impossible or too expensive to compute the likelihood exactly. In such settings, a common approach is to replace the likelihood with an approximation, and proceed with inference as if the approximate likelihood were the true likelihood. In this paper, we describe conditions which guarantee that such naive inference with an approximate likelihood has the same first‐order asymptotic properties as inference with the true likelihood. We investigate the implications of these results for inference using a Laplace approximation to the likelihood in a simple two‐level latent variable model and using reduced dependence approximations to the likelihood in an Ising model.