Latent Bayesian melding for integrating individual and population models

Mingjun Zhong* (Corresponding Author), Nigel Goddard, Charles Sutton

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

19 Citations (Scopus)

Abstract

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 singlechannel 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
Pages (from-to)3618-3626
Number of pages9
JournalAdvances in Neural Information Processing Systems
Volume2
DOIs
Publication statusPublished - 1 Dec 2015
Event29th Annual Conference on Neural Information Processing Systems, NIPS 2015 - Montreal, Canada
Duration: 7 Dec 201512 Dec 2015

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