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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 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 language | English |
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Title of host publication | Advances in Neural Information Processing Systems 28 (NIPS 2015) |
Pages | 3617-3625 |
Number of pages | 9 |
Publication status | Published - 2015 |
Event | Twenty-ninth Conference on Neural Information Processing Systems - Montreal, Canada Duration: 7 Dec 2015 → 12 Dec 2015 https://nips.cc/Conferences/2015 |
Conference
Conference | Twenty-ninth Conference on Neural Information Processing Systems |
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Abbreviated title | NIPS 2015 |
Country/Territory | Canada |
City | Montreal |
Period | 7/12/15 → 12/12/15 |
Internet address |
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Dive into the research topics of 'Latent Bayesian melding for integrating individual and population models'. Together they form a unique fingerprint.Projects
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Profiles
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Nigel Goddard
- School of Informatics - Reader
- Institute for Adaptive and Neural Computation - Director
- Global Environment and Society Academy - Steering Committee Member
- Data Science and Artificial Intelligence
Person: Academic: Research Active