Latent Bayesian melding for integrating individual and population models

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 28 (NIPS 2015)
Number of pages9
Publication statusPublished - 2015
EventTwenty-ninth Conference on Neural Information Processing Systems - Montreal, Canada
Duration: 7 Dec 201512 Dec 2015


ConferenceTwenty-ninth Conference on Neural Information Processing Systems
Abbreviated titleNIPS 2015
Internet address


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