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.

本文提出了一种隐含贝叶斯融合的方法，该方法是通过一个对数似然池框架下的个体层面和群体层面模型的分布平均来实现的，本文将其应用于电能分解中并证明该方法可以比单一的广义矩匹配方法具有更高的预测准确度。

潜在贝叶斯混合用于集成个体和人群模型