Abstract: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 ca… Show more
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