  Networks of low-cost sensors are becoming ubiquitous, but often suffer from
poor accuracies and drift. Regular colocation with reference sensors allows
recalibration but is complicated and expensive. Alternatively the calibration
can be transferred using low-cost, mobile sensors. However inferring the
calibration (with uncertainty) becomes difficult. We propose a variational
approach to model the calibration across the network. We demonstrate the
approach on synthetic and real air pollution data, and find it can perform
better than the state of the art (multi-hop calibration). We extend it to
categorical data produced by citizen-scientist labelling. In Summary: The
method achieves uncertainty-quantified calibration, which has been one of the
barriers to low-cost sensor deployment and citizen-science research.

Machine Learning