Latent Bayesian melding for integrating individual and population models
Latent Bayesian melding for integrating individual and population models
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发表时间：
2015-10
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通讯作者：
Mingjun Zhong;Nigel H. Goddard;Charles Sutton
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作者：
Mingjun Zhong;Nigel H. Goddard;Charles Sutton
在许多统计问题中，一个更粗粒度的模型可能适合于群体水平的行为，而一个更详细的模型适合于个人行为的准确建模。这就提出了如何整合这两种模式的问题。后验正则化等方法遵循广义矩匹配的思想，因为它们允许两个模型之间的期望匹配，但有时两个模型最方便地表示为潜变量模型。我们提出了潜在贝叶斯融合，这是出于平均分布在人口统计的个人水平和人口水平的模型下的对数意见池框架。在电力解聚，这是一种类型的单通道盲源分离问题的案例研究中，我们表明，潜在的贝叶斯融合导致显着更准确的预测比单纯基于广义矩匹配的方法。
In many statistical problems, a more coarse-grained model may be suitable for population-level behaviour, whereas a more detailed model is appropriate for accurate modelling of individual behaviour. This raises the question of how to integrate both types of models. Methods such as posterior regularization follow the idea of generalized moment matching, in that they allow matching expectations between two models, but sometimes both models are most conveniently expressed as latent variable models. We propose latent Bayesian melding, which is motivated by averaging the distributions over populations statistics of both the individual-level and the population-level models under a logarithmic opinion pool framework. In a case study on electricity disaggregation, which is a type of single-channel blind source separation problem, we show that latent Bayesian melding leads to significantly more accurate predictions than an approach based solely on generalized moment matching.