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Likelihood-free inference via classification

机译：通过分类进行无可能性推断

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摘要

Increasingly complex generative models are being used across disciplines as they allow for realistic characterization of data, but a common difficulty with them is the prohibitively large computational cost to evaluate the likelihood function and thus to perform likelihood-based statistical inference. A likelihood-free inference framework has emerged where the parameters are identified by finding values that yield simulated data resembling the observed data. While widely applicable, a major difficulty in this framework is how to measure the discrepancy between the simulated and observed data. Transforming the original problem into a problem of classifying the data into simulated versus observed, we find that classification accuracy can be used to assess the discrepancy. The complete arsenal of classification methods becomes thereby available for inference of intractable generative models. We validate our approach using theory and simulations for both point estimation and Bayesian inference, and demonstrate its use on real data by inferring an individual-based epidemiological model for bacterial infections in child care centers.
机译：跨学科使用越来越复杂的生成模型，因为它们允许对数据进行现实的表征，但是它们的共同困难是评估似然函数并因此执行基于似然的统计推断的计算量过大。出现了一种无可能性的推理框架，其中通过查找产生类似于观察数据的模拟数据的值来识别参数。尽管广泛适用，但此框架中的主要困难是如何衡量模拟数据与观察数据之间的差异。将原始问题转化为将数据分类为模拟数据与观察数据的问题，我们发现分类准确度可用于评估差异。因此，分类方法的完整工具库可用于推断顽固的生成模型。我们使用理论和模拟对点估计和贝叶斯推断进行了验证，并通过推断基于个人的流行病学模型对儿童保育中心的细菌感染证明了其在实际数据中的使用。

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